Deep Learning for Brain Tumor Segmentation: A Literature Review
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Literature Review (Secondary Research) Template
Student Name & CSU ID
Project Topic Title Deep learning neural networks for medical image segmentation of brain tumor diagnosis
Version 1.0 _ Week 1 (5 Journal Papers from CSU Library)
1
Reference in APA format that will be in
'Reference List'
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018). A deep learning model integrating FCNNs and
CRFs for brain tumor segmentation. Medical image analysis, Vol. 43, pp. 98-111.
Citation that will be in the content (Zhao, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S136184151730141X/1-s2.0-
S136184151730141X-main.pdf?
_tid=2da61e1d-7961-4740-9761-
0599e1e83c8b&acdnat=1532757075_b987
58be0e1f74b440498df61796621d
Q1 Conditional random fields, Brain tumour segmentation, Deep
learning, Fully convolutional neural networks
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: Integrating
Conditional Random Fields (CRFs) and
Fully Conventional Neural Networks
(FCNNs)
Problem: The efforts have been done for
developing automatic or semi-automatic brain
tumour segmentation techniques and the
preferred method is MRI. However, there are
few challenges of using MRI’s such as gliomas
ï‚· Voting based fusion method
ï‚· Image patches
ï‚· Image Slices
ï‚· Recurrent Neural Networks (RNNs)
ï‚· TC1 and TC2 scans
1
Student Name & CSU ID
Project Topic Title Deep learning neural networks for medical image segmentation of brain tumor diagnosis
Version 1.0 _ Week 1 (5 Journal Papers from CSU Library)
1
Reference in APA format that will be in
'Reference List'
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018). A deep learning model integrating FCNNs and
CRFs for brain tumor segmentation. Medical image analysis, Vol. 43, pp. 98-111.
Citation that will be in the content (Zhao, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S136184151730141X/1-s2.0-
S136184151730141X-main.pdf?
_tid=2da61e1d-7961-4740-9761-
0599e1e83c8b&acdnat=1532757075_b987
58be0e1f74b440498df61796621d
Q1 Conditional random fields, Brain tumour segmentation, Deep
learning, Fully convolutional neural networks
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: Integrating
Conditional Random Fields (CRFs) and
Fully Conventional Neural Networks
(FCNNs)
Problem: The efforts have been done for
developing automatic or semi-automatic brain
tumour segmentation techniques and the
preferred method is MRI. However, there are
few challenges of using MRI’s such as gliomas
ï‚· Voting based fusion method
ï‚· Image patches
ï‚· Image Slices
ï‚· Recurrent Neural Networks (RNNs)
ï‚· TC1 and TC2 scans
1
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Tools: 2D image patches, Image slices,
voting based fusion method, BRATS
Applied Area: Brain tumour diagnosis
treatment and planning
may appear similarly to gliosis and stroke in
MRI
Goal: The aim is to develop a model based on
deep learning based segmentation model by
using 2D image patches.
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Imaging Data:
The first step is to obtain the imaging data from
BRATS 2015, BRATS 2016
It helps to collect basic information about the
MRI results that scans diagnosed patients with
gliomas
Only small volume of data is collected for
analysis
2 Brain tumour segmentation using FCNNs:
In this, the extracted image patches are
categorised by trained CNNs.
It reduces the issue of sample imbalance at the
testing stage
The relationship among the patches can be
lost while using patch-based segmentation
technique
3 Pre-processing of imaging data:
Using robust intensity normalisation technique
in order to compare the MRI scans and using
N41TK for correcting MRI data
By correcting the bias in the MRI data,
accurate results are obtained.
The results may not be accurate if the data is
incorrectly modified.
4 Comparing the results:
The results obtained are compared with the MRI
data for checking the accuracy
It helps to ascertain the viability of using the
proposed method
NA
Validation Criteria (Measurement Criteria)
2
voting based fusion method, BRATS
Applied Area: Brain tumour diagnosis
treatment and planning
may appear similarly to gliosis and stroke in
MRI
Goal: The aim is to develop a model based on
deep learning based segmentation model by
using 2D image patches.
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Imaging Data:
The first step is to obtain the imaging data from
BRATS 2015, BRATS 2016
It helps to collect basic information about the
MRI results that scans diagnosed patients with
gliomas
Only small volume of data is collected for
analysis
2 Brain tumour segmentation using FCNNs:
In this, the extracted image patches are
categorised by trained CNNs.
It reduces the issue of sample imbalance at the
testing stage
The relationship among the patches can be
lost while using patch-based segmentation
technique
3 Pre-processing of imaging data:
Using robust intensity normalisation technique
in order to compare the MRI scans and using
N41TK for correcting MRI data
By correcting the bias in the MRI data,
accurate results are obtained.
The results may not be accurate if the data is
incorrectly modified.
4 Comparing the results:
The results obtained are compared with the MRI
data for checking the accuracy
It helps to ascertain the viability of using the
proposed method
NA
Validation Criteria (Measurement Criteria)
2
Dependent Variable Independent Variable
 Brain tumour segmentation  Voxel’s label
ï‚· Calculation of standard deviation ï‚· Collected MRI scans
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
Three segmentation
models are trained by
using 2D image
patches and
segmenting the brain
tumours using voting
based fusion method.
The results indicate
that the proposed
methods eliminate the
bias of MRI scan and
increase the efficiency
of the diagnosis.
The proposed method of training FCNNs and CRFs
helps to differentiate the gliosis and gliomas which
helps to provide appropriate diagnosis for cancer
patients.
The major drawback of applying this technique is
that the segmentation performance of the trained
network gets affected because the numbers of
pixels varies according to their class in the image
slices.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The current solution that is training FCNNs and
CRFs by using 2D image patches and segmenting
the images obtained from MRI scans.
The accuracy of the results obtained can be
evaluated from examining the scans which provides
appropriate analysis of the status of brain tumour.
There is a growing trend for using advanced
techniques for providing accurate diagnosis for
cancer. This research helps to understand the
techniques that can be used for developing future
technology in medical care.
Diagram/Flowchart
3
 Brain tumour segmentation  Voxel’s label
ï‚· Calculation of standard deviation ï‚· Collected MRI scans
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
Three segmentation
models are trained by
using 2D image
patches and
segmenting the brain
tumours using voting
based fusion method.
The results indicate
that the proposed
methods eliminate the
bias of MRI scan and
increase the efficiency
of the diagnosis.
The proposed method of training FCNNs and CRFs
helps to differentiate the gliosis and gliomas which
helps to provide appropriate diagnosis for cancer
patients.
The major drawback of applying this technique is
that the segmentation performance of the trained
network gets affected because the numbers of
pixels varies according to their class in the image
slices.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The current solution that is training FCNNs and
CRFs by using 2D image patches and segmenting
the images obtained from MRI scans.
The accuracy of the results obtained can be
evaluated from examining the scans which provides
appropriate analysis of the status of brain tumour.
There is a growing trend for using advanced
techniques for providing accurate diagnosis for
cancer. This research helps to understand the
techniques that can be used for developing future
technology in medical care.
Diagram/Flowchart
3
Figure: Flowchart of the proposed deep learning model integrating FCNNs and CRFs for brain tumor segmentation
4
4
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Figure: The network structure of deep FCNNs
5
5
2
Reference in APA format that will be in
'Reference List'
Soltaninejad, M., Yang, G., Lambrou, T., Allinson, N., Jones, T. L., Barrick, T. R., Howe, F.A., & Ye, X.
(2018). Supervised learning based multimodal MRI brain tumour segmentation using texture features from
supervoxels. Computer methods and programs in biomedicine, Vol. 157, pp. 69-84.
Citation that will be in the content (Soltaninejad, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S016926071731355X/1-s2.0-
S016926071731355X-main.pdf?
_tid=4a4dd16b-bfb6-41e4-b262-
b20025595ecb&acdnat=1532759931_766b
835216f08196959681dfbbd7ba82
Q1 Diffusion tensor imaging, Supervoxel, Brain tumour
segmentation, Multimodal MRI
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: 3D
supervoxel based learning method
Tools: Generating supervoxels, Gabor
Filters, random forests (RF) classifier
Applied Area: Brain tumour segmentation
Problem: Because of numerous types of
tumours types, is becomes difficult for
segmenting the brain tumour in MRI.
Goal: The aim is to develop a 3D supervoxel
based learning method in order to accurately
identify the type of tumour after conducting
MRI scans.
ï‚· Multimodal MRI images
ï‚· Isotropic component
ï‚· Anisotropic component
ï‚· Diffusion Tensor Imaging (DTI)
ï‚· Balanced Error Rate
6
Reference in APA format that will be in
'Reference List'
Soltaninejad, M., Yang, G., Lambrou, T., Allinson, N., Jones, T. L., Barrick, T. R., Howe, F.A., & Ye, X.
(2018). Supervised learning based multimodal MRI brain tumour segmentation using texture features from
supervoxels. Computer methods and programs in biomedicine, Vol. 157, pp. 69-84.
Citation that will be in the content (Soltaninejad, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S016926071731355X/1-s2.0-
S016926071731355X-main.pdf?
_tid=4a4dd16b-bfb6-41e4-b262-
b20025595ecb&acdnat=1532759931_766b
835216f08196959681dfbbd7ba82
Q1 Diffusion tensor imaging, Supervoxel, Brain tumour
segmentation, Multimodal MRI
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: 3D
supervoxel based learning method
Tools: Generating supervoxels, Gabor
Filters, random forests (RF) classifier
Applied Area: Brain tumour segmentation
Problem: Because of numerous types of
tumours types, is becomes difficult for
segmenting the brain tumour in MRI.
Goal: The aim is to develop a 3D supervoxel
based learning method in order to accurately
identify the type of tumour after conducting
MRI scans.
ï‚· Multimodal MRI images
ï‚· Isotropic component
ï‚· Anisotropic component
ï‚· Diffusion Tensor Imaging (DTI)
ï‚· Balanced Error Rate
6
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Generation of Supervoxels:
The multi-model MRI dataset is used for
generating supervoxels
It helps to collect and store the MRI images. The number of images used for analysing
results are limited
2 Calculating features of Supervoxels:
In this step, the features such as g histograms of
texton descriptor are calculated using Gabor
filters.
This process enables to calculate the features
of each supervoxel of different orientations.
Although MRI data is filtered, the accuracy
of using Gabor filters cannot be justified as
other techniques can be used.
3 Extracting statistical features:
After obtaining the images in the required
format, the statistical features are extracted.
This helps to analyse the MRI data accurately NA
4 Entering the features in Random Forests
(RF) classifier:
The statistical features are entered into RF
classifier for classifying each supervoxel as
healthy brain tissue, tumour core, and oedema.
By classifying the data, the accuracy of the
results can be enhanced.
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
ï‚· Training sample output ï‚· Structures of randomised tree in RF classifier
ï‚· Generating supervoxels ï‚· Data obtained from MRI dataset.
7
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Generation of Supervoxels:
The multi-model MRI dataset is used for
generating supervoxels
It helps to collect and store the MRI images. The number of images used for analysing
results are limited
2 Calculating features of Supervoxels:
In this step, the features such as g histograms of
texton descriptor are calculated using Gabor
filters.
This process enables to calculate the features
of each supervoxel of different orientations.
Although MRI data is filtered, the accuracy
of using Gabor filters cannot be justified as
other techniques can be used.
3 Extracting statistical features:
After obtaining the images in the required
format, the statistical features are extracted.
This helps to analyse the MRI data accurately NA
4 Entering the features in Random Forests
(RF) classifier:
The statistical features are entered into RF
classifier for classifying each supervoxel as
healthy brain tissue, tumour core, and oedema.
By classifying the data, the accuracy of the
results can be enhanced.
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
ï‚· Training sample output ï‚· Structures of randomised tree in RF classifier
ï‚· Generating supervoxels ï‚· Data obtained from MRI dataset.
7
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Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
The MRI images are
collected for analysis
and supervoxels are
generated. Before
entering the data into
RF classifier, Gabor
Filters are used for
classifying the data.
The results obtained
by performing the
tests indicate that the
images provide
accurate results as to
the condition of the
tumour.
With the increasing number of cases related to brain
tumour, it is essential for developing a technology
which helps to accurately identify the tumours for
providing appropriate diagnosis.
The major drawback of using supervoxel is that
there is a minimum criterion that has to be fulfilled
with regards to its parameters.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The proposed 3D supervoxel based learning
method helps to eliminate the issues of MRI
scans. This is essential for ascertaining the actual
nature of tumour.
The MRI images are accurately classified as healthy
brain tissue, tumour core, and oedema which is
helpful while designing the type of diagnosis that is
to be provided to the patients.
3D supervoxel based learning method is a solution
for the issues currently being faced related to MRI
scans. This helps to develop an effective neural
networks and image processing technique.
Diagram/Flowchart
Figure: Flowchart of the proposed multimodal MRI segmentation method for segmentation of brain tumour.
8
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
The MRI images are
collected for analysis
and supervoxels are
generated. Before
entering the data into
RF classifier, Gabor
Filters are used for
classifying the data.
The results obtained
by performing the
tests indicate that the
images provide
accurate results as to
the condition of the
tumour.
With the increasing number of cases related to brain
tumour, it is essential for developing a technology
which helps to accurately identify the tumours for
providing appropriate diagnosis.
The major drawback of using supervoxel is that
there is a minimum criterion that has to be fulfilled
with regards to its parameters.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The proposed 3D supervoxel based learning
method helps to eliminate the issues of MRI
scans. This is essential for ascertaining the actual
nature of tumour.
The MRI images are accurately classified as healthy
brain tissue, tumour core, and oedema which is
helpful while designing the type of diagnosis that is
to be provided to the patients.
3D supervoxel based learning method is a solution
for the issues currently being faced related to MRI
scans. This helps to develop an effective neural
networks and image processing technique.
Diagram/Flowchart
Figure: Flowchart of the proposed multimodal MRI segmentation method for segmentation of brain tumour.
8
Figure: Flowchart of the multimodal normalization and histogram matching of the
MR dataset
9
MR dataset
9
3
Reference in APA format that will be in
'Reference List'
Pinto, A., Pereira, S., Rasteiro, D., & Silva, C. A. (2018). Hierarchical Brain Tumour Segmentation using
Extremely Randomized Trees. Pattern Recognition.
Citation that will be in the content (Pinto, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S0031320318301699/1-s2.0-
S0031320318301699-main.pdf?
_tid=6cec5e43-2f0c-46c9-941d-
276563fcb78d&acdnat=1532759868_53c8
b955cadbe0e1d6b5046a6a61f0c7
Q1 Magnetic resonance imaging, Extremely randomized trees,
Machine learning, Image segmentation
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: Automatic
hierarchical brain tumour segmentation
using Extremely Randomized Trees (ERT)
Tools: Binary classification, morphological
refinement, N4ITK method, histogram
matching method
Applied Area: Brain Tumour
Segmentation
Problem: MRI is the commonly used method
for identifying gliomas. But this requires
manual segmentation which is difficult as well
as time-consuming task.
Goal: The aim is to develop an automatic
tumour segmentation technique using
Extremely Randomized Trees for accurate
analysis of the condition of tumour.
ï‚· BRATS 2013
ï‚· Bias field distortion
ï‚· Intensity normalisation
ï‚· Data augmentation
ï‚· 2-dimensional patches
ï‚· Tumour detection
10
Reference in APA format that will be in
'Reference List'
Pinto, A., Pereira, S., Rasteiro, D., & Silva, C. A. (2018). Hierarchical Brain Tumour Segmentation using
Extremely Randomized Trees. Pattern Recognition.
Citation that will be in the content (Pinto, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S0031320318301699/1-s2.0-
S0031320318301699-main.pdf?
_tid=6cec5e43-2f0c-46c9-941d-
276563fcb78d&acdnat=1532759868_53c8
b955cadbe0e1d6b5046a6a61f0c7
Q1 Magnetic resonance imaging, Extremely randomized trees,
Machine learning, Image segmentation
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: Automatic
hierarchical brain tumour segmentation
using Extremely Randomized Trees (ERT)
Tools: Binary classification, morphological
refinement, N4ITK method, histogram
matching method
Applied Area: Brain Tumour
Segmentation
Problem: MRI is the commonly used method
for identifying gliomas. But this requires
manual segmentation which is difficult as well
as time-consuming task.
Goal: The aim is to develop an automatic
tumour segmentation technique using
Extremely Randomized Trees for accurate
analysis of the condition of tumour.
ï‚· BRATS 2013
ï‚· Bias field distortion
ï‚· Intensity normalisation
ï‚· Data augmentation
ï‚· 2-dimensional patches
ï‚· Tumour detection
10
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The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Collecting the data:
The MRI images are collected for analysing the
accuracy of using Extremely Randomized Trees.
It helps to ascertain the accuracy that can be
achieved by using the proposed technique.
A large sample has to be analysed for
accurate analysis of the data.
2 Correcting the image bias:
The imperfections in the MRI images are
corrected using N4ITK method
This minimises the distortions in the images
and enhances the accuracy of the results.
Although the imperfections are reduced, it
does not help to completely eliminate wrong
data.
3 Intensity Normalisation:
The histogram matching method is used during
this stage for ensuring identical intensity
distribution of the same sequence
This enables reducing the differences between
the images
NA
4 Analysis of the data:
The results are compared with existing
techniques used such as T1-weighted, and d
Fluid Attenuation Inversion Recovery
This helps to understand the viability of using
this technique.
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
ï‚· Tumour classifications ï‚· Complexity of the tumour
ï‚· Calculation of intensities ï‚· intensity variability of the percentiles of the training images
Input and Output Critical Thinking: Feature of this work, and Critical Thinking: Limitations of the research
11
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Collecting the data:
The MRI images are collected for analysing the
accuracy of using Extremely Randomized Trees.
It helps to ascertain the accuracy that can be
achieved by using the proposed technique.
A large sample has to be analysed for
accurate analysis of the data.
2 Correcting the image bias:
The imperfections in the MRI images are
corrected using N4ITK method
This minimises the distortions in the images
and enhances the accuracy of the results.
Although the imperfections are reduced, it
does not help to completely eliminate wrong
data.
3 Intensity Normalisation:
The histogram matching method is used during
this stage for ensuring identical intensity
distribution of the same sequence
This enables reducing the differences between
the images
NA
4 Analysis of the data:
The results are compared with existing
techniques used such as T1-weighted, and d
Fluid Attenuation Inversion Recovery
This helps to understand the viability of using
this technique.
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
ï‚· Tumour classifications ï‚· Complexity of the tumour
ï‚· Calculation of intensities ï‚· intensity variability of the percentiles of the training images
Input and Output Critical Thinking: Feature of this work, and Critical Thinking: Limitations of the research
11
Why (Justify) current solution, and Why (Justify)
Input (Data) Output (View)
For arriving at the
desired results, the
MRI scan images are
collected. The bias in
the images are
normalised before
entering the same into
Random Forest (RF
classifier).
The output is verified
by considering the
metrics, Positive
Predictive Value,
Dice Similarity
Coefficient and
Sensitivity.
The proposed technique helps to analyse the types
of tumour by eliminating biases. Hence, accuracy of
the diagnosis is ensured.
The intensity range of the necrosis tissues would
have been shifted affecting the training of
classifier.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The use of the proposed technique reduces the
manual segmentation of tumour tissues thereby
enhancing the accuracy of the results.
The images are clearer and the affected brain area is
highlighted along with corresponding tissues.
The proposed research will help to develop a
novel technique which can be used for developing
advanced methods for detecting brain tumours.
Diagram/Flowchart
12
Input (Data) Output (View)
For arriving at the
desired results, the
MRI scan images are
collected. The bias in
the images are
normalised before
entering the same into
Random Forest (RF
classifier).
The output is verified
by considering the
metrics, Positive
Predictive Value,
Dice Similarity
Coefficient and
Sensitivity.
The proposed technique helps to analyse the types
of tumour by eliminating biases. Hence, accuracy of
the diagnosis is ensured.
The intensity range of the necrosis tissues would
have been shifted affecting the training of
classifier.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The use of the proposed technique reduces the
manual segmentation of tumour tissues thereby
enhancing the accuracy of the results.
The images are clearer and the affected brain area is
highlighted along with corresponding tissues.
The proposed research will help to develop a
novel technique which can be used for developing
advanced methods for detecting brain tumours.
Diagram/Flowchart
12
Figure: Overview of the proposed method
Figure: T1c sequence of a HGG patient in the different stages of the pre-processing step. In the bias field heat map, warmer colours represent a
higher magnitude of the bias field
13
Figure: T1c sequence of a HGG patient in the different stages of the pre-processing step. In the bias field heat map, warmer colours represent a
higher magnitude of the bias field
13
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4
Reference in APA format that will be in
'Reference List'
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Gray, R., Doel, T., Yipeng, H., & Whyntie, T.
(2018). NiftyNet: a deep-learning platform for medical imaging. Computer methods and programs in
biomedicine, Vol. 158, pp. 113-122.
Citation that will be in the content (Gibson, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S0169260717311823/1-s2.0-
S0169260717311823-main.pdf?
_tid=e9244757-9c0d-4c85-8389-
29326062c28c&acdnat=1532759777_7f87
d9a18241216c2289274709be4746
Q1 Convolutional neural network, Image regression, Deep
learning, Generative adversarial network
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: Using
TensorFlow framework for developing
NiftyNet platform.
Tools: Image regression, segmentation of
multiple abdominal organs, TensorBoard
visualization
Problem: Although deep learning platforms
are developed, they do not provide specific
functionality which requires huge
implementation efforts.
Goal: The aim is to simplify the learning
platforms so that further research can be
conducted for improving the quality of the
medical services.
ï‚· Image regression
ï‚· Segmentation
ï‚· Brain magnetic resonance images
ï‚· Specified anatomical poses.
ï‚· Representation learning applications
14
Reference in APA format that will be in
'Reference List'
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Gray, R., Doel, T., Yipeng, H., & Whyntie, T.
(2018). NiftyNet: a deep-learning platform for medical imaging. Computer methods and programs in
biomedicine, Vol. 158, pp. 113-122.
Citation that will be in the content (Gibson, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S0169260717311823/1-s2.0-
S0169260717311823-main.pdf?
_tid=e9244757-9c0d-4c85-8389-
29326062c28c&acdnat=1532759777_7f87
d9a18241216c2289274709be4746
Q1 Convolutional neural network, Image regression, Deep
learning, Generative adversarial network
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: Using
TensorFlow framework for developing
NiftyNet platform.
Tools: Image regression, segmentation of
multiple abdominal organs, TensorBoard
visualization
Problem: Although deep learning platforms
are developed, they do not provide specific
functionality which requires huge
implementation efforts.
Goal: The aim is to simplify the learning
platforms so that further research can be
conducted for improving the quality of the
medical services.
ï‚· Image regression
ï‚· Segmentation
ï‚· Brain magnetic resonance images
ï‚· Specified anatomical poses.
ï‚· Representation learning applications
14
Applied Area: Developing deep learning
platforms.
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Developing the infrastructure:
This stage involves designing various functions
such as image segmentation, generation and
regression.
This will help the researchers for developing
advanced functions.
This involves expert knowledge and
incurring huge expenses
2 Gathering the required data:
For developing an effective deep learning
platform, it is necessary to gather a large number
of images.
Collecting and storing information enables to
analyse different types of data.
The challenge is to store the information at
one location.
3 Formatting the images:
Storing the images in a suitable format
depending on the type of application being used.
Formatting ensures easy integration of images Storing the images in unsupported format
may result in losing valuable data.
4 Analysing the stored data:
The stored images are reviewed and the time
taken for accessing these images is ascertained.
This helps to ascertain the effectiveness of the
proposed system
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
ï‚· Data augmentation ï‚· Type of images saved
ï‚· Sampling and output handling ï‚· Training performed
15
platforms.
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Developing the infrastructure:
This stage involves designing various functions
such as image segmentation, generation and
regression.
This will help the researchers for developing
advanced functions.
This involves expert knowledge and
incurring huge expenses
2 Gathering the required data:
For developing an effective deep learning
platform, it is necessary to gather a large number
of images.
Collecting and storing information enables to
analyse different types of data.
The challenge is to store the information at
one location.
3 Formatting the images:
Storing the images in a suitable format
depending on the type of application being used.
Formatting ensures easy integration of images Storing the images in unsupported format
may result in losing valuable data.
4 Analysing the stored data:
The stored images are reviewed and the time
taken for accessing these images is ascertained.
This helps to ascertain the effectiveness of the
proposed system
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
ï‚· Data augmentation ï‚· Type of images saved
ï‚· Sampling and output handling ï‚· Training performed
15
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
For developing
NiftyNet platform, the
data has to be
collected and stored
for future analysis and
research.
The output is that the
data is easily
accessible and the
suitable information
can be obtained at
minimum duration of
time.
NiftyNet platform provides the solution faced for
developing a suitable deep learning platform for
improving medical care services.
The major concern is the storing large volumes of
data at one location because usually these require
specialised storage devices.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
NiftyNet helps to develop deep learning platform
by collecting the required information. Also, the
images helps to analyse the opportunities that can
be utilised for improving medical care.
The images stored are easily available and
accessible for further research.
NiftyNet helps to understand the importance of
developing deep learning platform which is
helpful for developing new methods that can be
used for brain tumour diagnosis.
Diagram/Flowchart
16
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
For developing
NiftyNet platform, the
data has to be
collected and stored
for future analysis and
research.
The output is that the
data is easily
accessible and the
suitable information
can be obtained at
minimum duration of
time.
NiftyNet platform provides the solution faced for
developing a suitable deep learning platform for
improving medical care services.
The major concern is the storing large volumes of
data at one location because usually these require
specialised storage devices.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
NiftyNet helps to develop deep learning platform
by collecting the required information. Also, the
images helps to analyse the opportunities that can
be utilised for improving medical care.
The images stored are easily available and
accessible for further research.
NiftyNet helps to understand the importance of
developing deep learning platform which is
helpful for developing new methods that can be
used for brain tumour diagnosis.
Diagram/Flowchart
16
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Figure: Data flow implemented in typical deep learning projects
17
17
Figure: A brief overview of NiftyNet components
5
Reference in APA format that will be in
'Reference List'
Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J. B., & Meyer, P. (2018). Automatic detection and
segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.
Computers in biology and medicine, 95, 43-54.
Citation that will be in the content (Charron, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn- Q2 Convolutional neural network, Brain metastases, Magnetic
18
5
Reference in APA format that will be in
'Reference List'
Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J. B., & Meyer, P. (2018). Automatic detection and
segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.
Computers in biology and medicine, 95, 43-54.
Citation that will be in the content (Charron, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn- Q2 Convolutional neural network, Brain metastases, Magnetic
18
com.ezproxy.csu.edu.au/
S0010482518300325/1-s2.0-
S0010482518300325-main.pdf?
_tid=8ab2cdc7-fd92-45a8-bf3d-
da4e799ed122&acdnat=1532759589_c1f7e
affa2beb33b36a92103aeac0f7a
resonance imaging,
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: Automatic
segmentation of brain metastases
Tools: 3D convolutional neural network
(DeepMedic), 3DT1Gd MRI, Brain
Extraction Tool
Applied Area: Brain tumour diagnosis
Problem: There are various methods that have
been developed for segmentation of gliomas,
but there are no comprehensive studies for
metastases.
Goal: The aim is to detect and segment brain
metastases in MRI by using existing 3D
convolutional neural network
ï‚· Different MRI modalities
ï‚· Detection and Segmentation
ï‚· Necrotic parts
ï‚· Spatial resolution
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Adapting network parameters:
This involves adapting network parameters for
brain metastases.
This is helpful while evaluating the results There are no standard techniques and hence,
parameters have to be carefully selected.
2 Using MRI modalities: It helps to evaluate the network performance
related to segmentation and detection
Accuracy depends on the collected MRI
data.
19
S0010482518300325/1-s2.0-
S0010482518300325-main.pdf?
_tid=8ab2cdc7-fd92-45a8-bf3d-
da4e799ed122&acdnat=1532759589_c1f7e
affa2beb33b36a92103aeac0f7a
resonance imaging,
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name: Automatic
segmentation of brain metastases
Tools: 3D convolutional neural network
(DeepMedic), 3DT1Gd MRI, Brain
Extraction Tool
Applied Area: Brain tumour diagnosis
Problem: There are various methods that have
been developed for segmentation of gliomas,
but there are no comprehensive studies for
metastases.
Goal: The aim is to detect and segment brain
metastases in MRI by using existing 3D
convolutional neural network
ï‚· Different MRI modalities
ï‚· Detection and Segmentation
ï‚· Necrotic parts
ï‚· Spatial resolution
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Adapting network parameters:
This involves adapting network parameters for
brain metastases.
This is helpful while evaluating the results There are no standard techniques and hence,
parameters have to be carefully selected.
2 Using MRI modalities: It helps to evaluate the network performance
related to segmentation and detection
Accuracy depends on the collected MRI
data.
19
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In this, using single or combined use of various
MRI modalities is explored.
3 Using additional database:
The additional information is used for separating
active parts of metastases from necrotic ones.
This reduces the risk of prescribing incorrect
diagnosis.
NA
4 Evaluating the effectiveness:
The results are evaluated for ascertaining the
benefits of using the proposed approach.
This enables for conducting further research. NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
ï‚· Segmentation maps ï‚· Performance of the network
ï‚· Adapting network parameters ï‚· MRI database
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
For studying the
usefulness of
DeepMedic, it is
essential for using
MRI modalities for
detecting brain
metastases.
The results showcased
that the use of
multimodal MRI
enables to detect brain
metastases.
Brain metastases have to be detected since it affects
cancer patients and early diagnosis helps to cure
cancer. Hence, by using the proposed technique,
metastases can be detected.
The major concern with the research is the very
small size of the dataset. This affects the accuracy
of the results obtained.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
20
MRI modalities is explored.
3 Using additional database:
The additional information is used for separating
active parts of metastases from necrotic ones.
This reduces the risk of prescribing incorrect
diagnosis.
NA
4 Evaluating the effectiveness:
The results are evaluated for ascertaining the
benefits of using the proposed approach.
This enables for conducting further research. NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
ï‚· Segmentation maps ï‚· Performance of the network
ï‚· Adapting network parameters ï‚· MRI database
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
For studying the
usefulness of
DeepMedic, it is
essential for using
MRI modalities for
detecting brain
metastases.
The results showcased
that the use of
multimodal MRI
enables to detect brain
metastases.
Brain metastases have to be detected since it affects
cancer patients and early diagnosis helps to cure
cancer. Hence, by using the proposed technique,
metastases can be detected.
The major concern with the research is the very
small size of the dataset. This affects the accuracy
of the results obtained.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
20
for your project
3D convolutional neural network can be used for
detecting metastases which is essential because of
increasing number of cancer patients.
The effectiveness of this technique helps to detect
and segment brain metastases on multimodal MRI.
The use of 3D convolutional neural network
(DeepMedic) while using multimodal MRI
enables to develop technologies for providing
medical care for patients suffering from brain
tumour.
Diagram/Flowchart
Figure: From left to right: cerebral metastasis visualized on 2DT1, 3DT1Gd and 2DFLAIR MRI
21
3D convolutional neural network can be used for
detecting metastases which is essential because of
increasing number of cancer patients.
The effectiveness of this technique helps to detect
and segment brain metastases on multimodal MRI.
The use of 3D convolutional neural network
(DeepMedic) while using multimodal MRI
enables to develop technologies for providing
medical care for patients suffering from brain
tumour.
Diagram/Flowchart
Figure: From left to right: cerebral metastasis visualized on 2DT1, 3DT1Gd and 2DFLAIR MRI
21
Fig: Histogram of the brain metastasis diameters and volumes included in our database.
22
22
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Version 2.0 _ Week 2 (10 Papers = 5 NEW Journal Papers + Updated pervious 5 papers that you worked on them in Version 1.0)
6
Reference in APA format that will be in
'Reference List'
Hussain, S., Anwar, S. M., & Majid, M. (2018). Segmentation of glioma tumours in brain using deep
convolutional neural network. Neurocomputing, 282, 248-261.
Citation that will be in the content (Hussain, et.al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S0925231217318763/1-s2.0-
S0925231217318763-main.pdf?
_tid=7abf2a11-30ee-4ea4-9970-
2a9421028c2b&acdnat=1532766178_ce0b
22e219896889e97dc491477f9126
Q1 Segmentation, Convolutional Neural networks, Brain tumour,
Deep learning
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Automated brain tumor segmentation
algorithm
Tools:
BRATS 2013, BRATS 2015 datasets
Applied Area:
Medical industry
Problem:
The main problem in the detection of brain
tumour is the using the segmentation approach.
Goal:
The goal of the research is to develop a model
that will detect the tumour and diagnose it at
the early stage so that it can be cured.
ï‚· Magnetic resonance imaging (MRI)
ï‚· computed tomography (CT)
ï‚· positron emission tomography (PET)
ï‚· magnetic resonance spectroscopy (MRS)
ï‚· cerebrospinal fluid (CSF)
ï‚· BRATS
ï‚· MICCAI
The Process (Mechanism) of this Work; The process steps of the Technique/system
23
6
Reference in APA format that will be in
'Reference List'
Hussain, S., Anwar, S. M., & Majid, M. (2018). Segmentation of glioma tumours in brain using deep
convolutional neural network. Neurocomputing, 282, 248-261.
Citation that will be in the content (Hussain, et.al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S0925231217318763/1-s2.0-
S0925231217318763-main.pdf?
_tid=7abf2a11-30ee-4ea4-9970-
2a9421028c2b&acdnat=1532766178_ce0b
22e219896889e97dc491477f9126
Q1 Segmentation, Convolutional Neural networks, Brain tumour,
Deep learning
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Automated brain tumor segmentation
algorithm
Tools:
BRATS 2013, BRATS 2015 datasets
Applied Area:
Medical industry
Problem:
The main problem in the detection of brain
tumour is the using the segmentation approach.
Goal:
The goal of the research is to develop a model
that will detect the tumour and diagnose it at
the early stage so that it can be cured.
ï‚· Magnetic resonance imaging (MRI)
ï‚· computed tomography (CT)
ï‚· positron emission tomography (PET)
ï‚· magnetic resonance spectroscopy (MRS)
ï‚· cerebrospinal fluid (CSF)
ï‚· BRATS
ï‚· MICCAI
The Process (Mechanism) of this Work; The process steps of the Technique/system
23
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Pre-processing-
A technique which is non-uniform and non-
parametric is used here to normalize the
intensity and removal of artefact in MRI.
Artefacts such as motion and in homogeneity
of the field is removed
NA
2 Convolutional Neural Networks-
The convolutional layers are overlapped to form
hierarchical fashion for the feature of maps.
It helps in development of image recognition. Computation requirements
3 Post-processing-
Use of morphological operators is done here to
enhance segmentation results
Removal of small and false positives is done
around the corners of a image which is
segmented
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Scanning the skull for imaging MRI
Deals with multiple predictions CRF
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
Input in this particular
research is to develop
a model help the
Output from this
particular research is
that an automated
Tumor regions generally are placed near white
matter fiber and they have blurry boundaries which
makes it difficult to detect. The correct
segmentation of the brain tumor is very crucial in
the diagnosis and the treatment planning. Therefore,
The main limitation of this scheme is that its
capacity is limited. If the capacity of the system is
increased then the performance could be
enhanced. Further, this area of study can be
applied to other fields such as analysis of
24
1 Pre-processing-
A technique which is non-uniform and non-
parametric is used here to normalize the
intensity and removal of artefact in MRI.
Artefacts such as motion and in homogeneity
of the field is removed
NA
2 Convolutional Neural Networks-
The convolutional layers are overlapped to form
hierarchical fashion for the feature of maps.
It helps in development of image recognition. Computation requirements
3 Post-processing-
Use of morphological operators is done here to
enhance segmentation results
Removal of small and false positives is done
around the corners of a image which is
segmented
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Scanning the skull for imaging MRI
Deals with multiple predictions CRF
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
Input in this particular
research is to develop
a model help the
Output from this
particular research is
that an automated
Tumor regions generally are placed near white
matter fiber and they have blurry boundaries which
makes it difficult to detect. The correct
segmentation of the brain tumor is very crucial in
the diagnosis and the treatment planning. Therefore,
The main limitation of this scheme is that its
capacity is limited. If the capacity of the system is
increased then the performance could be
enhanced. Further, this area of study can be
applied to other fields such as analysis of
24
automation in the
segmentation of the
brain tumour.
brain tumour
segmentation scheme
is proposed that is
based on deep
convolutional neural
network.
automated brain tumor segmentation technique is
proposed to improve the segmentation of the tumor.
biomedical image analysis and the segmentation
of brain lesion.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
Introduction of automated segmentation of the
tumor in brain can help in the diagnosis of the
tumor and future treatment of this tumor. The
technique on which the segmentation is based on
is the Deep Convolutional Neural Networks.
In this system, the two networks are placed one
above another to make a new Tlinear nexus
architecture which can be used to get the best
results out of all the architectures present. The first
network contains the layers placed parallel and the
second network contains the layers linearly.
After a number of studies and evaluation done on
different architectures, the various settings have
been explored validation is given to the use of
parallel paths in an architecture. The results done
on two datasets i.e. BRATS2013 and BRATS2015
have shown that the performance is increased in
comparison with other state-of-art techniques.
Diagram/Flowchart
25
segmentation of the
brain tumour.
brain tumour
segmentation scheme
is proposed that is
based on deep
convolutional neural
network.
automated brain tumor segmentation technique is
proposed to improve the segmentation of the tumor.
biomedical image analysis and the segmentation
of brain lesion.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
Introduction of automated segmentation of the
tumor in brain can help in the diagnosis of the
tumor and future treatment of this tumor. The
technique on which the segmentation is based on
is the Deep Convolutional Neural Networks.
In this system, the two networks are placed one
above another to make a new Tlinear nexus
architecture which can be used to get the best
results out of all the architectures present. The first
network contains the layers placed parallel and the
second network contains the layers linearly.
After a number of studies and evaluation done on
different architectures, the various settings have
been explored validation is given to the use of
parallel paths in an architecture. The results done
on two datasets i.e. BRATS2013 and BRATS2015
have shown that the performance is increased in
comparison with other state-of-art techniques.
Diagram/Flowchart
25
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Figure: Block diagram of the proposed methodology
7
Reference in APA format that will be in
'Reference List'
Rachmadi, M. F., Valdés-Hernández, M. D. C., Agan, M. L. F., Di Perri, C., Komura, T., & Alzheimer's
Disease Neuroimaging Initiative. (2018). Segmentation of white matter hyperintensities using convolutional
neural networks with global spatial information in routine clinical brain MRI with none or mild vascular
pathology. Computerized Medical Imaging and Graphics, 66, 28-43.
Citation that will be in the content (Rachmadi, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S089561111830082X/1-s2.0-
S089561111830082X-main.pdf?
_tid=f3f69cb0-469c-4b3a-9dfd-
5c67f9ac14e2&acdnat=1532767949_bff14
e82310fe9bdbe76018662fbbce3
Q2 Mild cognitive impairment, Convolutional neural network,
White matter hyper intensities, Alzheimer's disease, Deep
learning, Segmentation, Global spatial information
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Convolutional neural network (CNN)
Tools:
Linear nexus (LN), Two-path linear nexus
(TLinear)
Problem:
Small area or volume of white matter hyper
intensities are the main problem occurred in
the brain MRI and authors are focused on the
same.
Goal:
ï‚· Glioblastoma multiforme (GBM)
ï‚· Markov random field (MRF)
ï‚· Local histograms
ï‚· Raw pixel values
ï‚· Fuzzy C-means (FCM)
ï‚· Two-path Nexus (TPN)
ï‚· Linear nexus (LN)
26
7
Reference in APA format that will be in
'Reference List'
Rachmadi, M. F., Valdés-Hernández, M. D. C., Agan, M. L. F., Di Perri, C., Komura, T., & Alzheimer's
Disease Neuroimaging Initiative. (2018). Segmentation of white matter hyperintensities using convolutional
neural networks with global spatial information in routine clinical brain MRI with none or mild vascular
pathology. Computerized Medical Imaging and Graphics, 66, 28-43.
Citation that will be in the content (Rachmadi, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S089561111830082X/1-s2.0-
S089561111830082X-main.pdf?
_tid=f3f69cb0-469c-4b3a-9dfd-
5c67f9ac14e2&acdnat=1532767949_bff14
e82310fe9bdbe76018662fbbce3
Q2 Mild cognitive impairment, Convolutional neural network,
White matter hyper intensities, Alzheimer's disease, Deep
learning, Segmentation, Global spatial information
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Convolutional neural network (CNN)
Tools:
Linear nexus (LN), Two-path linear nexus
(TLinear)
Problem:
Small area or volume of white matter hyper
intensities are the main problem occurred in
the brain MRI and authors are focused on the
same.
Goal:
ï‚· Glioblastoma multiforme (GBM)
ï‚· Markov random field (MRF)
ï‚· Local histograms
ï‚· Raw pixel values
ï‚· Fuzzy C-means (FCM)
ï‚· Two-path Nexus (TPN)
ï‚· Linear nexus (LN)
26
Applied Area:
Medical industry
To enhance the brain MRI procedure by
introducing convolutional networks scheme.
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Training and testing processes-
In this process, all the gathered database got
tested and used for the training session and
further got evaluated by Fazekas score.
Accuracy of Machine learning algorithm NA
2 Parameter setup-
All the important parameters for machine
learning got evaluate before the training process.
Accurate classification by Radial basis (RBF)
kernel
NA
3 Evaluation-
In the process, results of medical image
segmentation got evaluated.
Precision of the equation NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
T1 and T2 Nature of the tissues imaged
WMH partly On their location in the brain
statistical analyses grey-level histogram
Input and Output Critical Thinking: Feature of this work, and Critical Thinking: Limitations of the research
27
Medical industry
To enhance the brain MRI procedure by
introducing convolutional networks scheme.
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Training and testing processes-
In this process, all the gathered database got
tested and used for the training session and
further got evaluated by Fazekas score.
Accuracy of Machine learning algorithm NA
2 Parameter setup-
All the important parameters for machine
learning got evaluate before the training process.
Accurate classification by Radial basis (RBF)
kernel
NA
3 Evaluation-
In the process, results of medical image
segmentation got evaluated.
Precision of the equation NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
T1 and T2 Nature of the tissues imaged
WMH partly On their location in the brain
statistical analyses grey-level histogram
Input and Output Critical Thinking: Feature of this work, and Critical Thinking: Limitations of the research
27
Why (Justify) current solution, and Why (Justify)
Input (Data) Output (View)
Scheme of learning
which help to getting
information about
features of MIRs.
That could help to
enhance MIR
procedure in brain
tumour decease.
To develop and use
the deep learning
algorithms to for
betterment of
segmentation of
WMH.
Feature of the work is various deep learning
algorithms such as SVR and RF. These proposed
algorithms assist in brain related decease and
segmentation of WMH
The proposed techniques and algorithms used in
the proposal are CNN -GIS and they need to be
evaluated in the various fields and advancement
also required in the same.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
Research was focused on the brain tumor MIRs
and the algorithms which made the process of
WMH segmentation ease and advance, have been
discussed (Rachmadi, et. al., 2018).
Brain patients and doctors are the end users of the
proposed algorithms as it assists process of WMH
segmentation and MRI of brain tumour.
CNNs are the effective and useful algorithm in the
way of WMH segmentations and also assisting in
routine clinical brain MRIs.
Diagram/Flowchart
28
Input (Data) Output (View)
Scheme of learning
which help to getting
information about
features of MIRs.
That could help to
enhance MIR
procedure in brain
tumour decease.
To develop and use
the deep learning
algorithms to for
betterment of
segmentation of
WMH.
Feature of the work is various deep learning
algorithms such as SVR and RF. These proposed
algorithms assist in brain related decease and
segmentation of WMH
The proposed techniques and algorithms used in
the proposal are CNN -GIS and they need to be
evaluated in the various fields and advancement
also required in the same.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
Research was focused on the brain tumor MIRs
and the algorithms which made the process of
WMH segmentation ease and advance, have been
discussed (Rachmadi, et. al., 2018).
Brain patients and doctors are the end users of the
proposed algorithms as it assists process of WMH
segmentation and MRI of brain tumour.
CNNs are the effective and useful algorithm in the
way of WMH segmentations and also assisting in
routine clinical brain MRIs.
Diagram/Flowchart
28
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Figure: Example image of WMH visualisation in two different types of MRI structural sequences: T2 based-fluid attenuated inversion recovery
8
Reference in APA format that will be in
'Reference List'
Izadyyazdanabadi, M., Belykh, E., Mooney, M., Martirosyan, N., Eschbacher, J., Nakaji, P., ... & Yang, Y.
(2018). Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic
localization for neurosurgical CLE images. Journal of Visual Communication and Image Representation, 54,
10-20.
Citation that will be in the content (Izadyyazdanabadi, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
Q1 Ensemble modelling, Confocal laser endomicroscopy, Neural
network, Surgical vision, Unsupervised localization, Brain
29
8
Reference in APA format that will be in
'Reference List'
Izadyyazdanabadi, M., Belykh, E., Mooney, M., Martirosyan, N., Eschbacher, J., Nakaji, P., ... & Yang, Y.
(2018). Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic
localization for neurosurgical CLE images. Journal of Visual Communication and Image Representation, 54,
10-20.
Citation that will be in the content (Izadyyazdanabadi, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
Q1 Ensemble modelling, Confocal laser endomicroscopy, Neural
network, Surgical vision, Unsupervised localization, Brain
29
S1047320318300804/1-s2.0-
S1047320318300804-main.pdf?
_tid=a27da700-7e16-423e-b435-
1861656b6fe2&acdnat=1532770818_9e6d
6243cbedae01e13d217defad7166
tumor
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Deep learning image selection algorithm
Tools:
Confocal imaging instrument instrument,
intraoperative data collection process
Applied Area:
Medical industry
Problem:
Confocal Laser Endomicroscopy (CLE) is the
advanced method in the brain tumor imaging.
But sometimes a large number of CLE images
can be distorted.
Goal:
The goal is to automatically select a diagnostic
image for examination.
ï‚· Fluorescein sodium (FNa)
ï‚· Rectified linear unit (ReLU)
ï‚· Normalization layer
ï‚· Activation layer
ï‚· Stochastic Gradient Descent (SGD)
ï‚· Convolutional layers
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Dataset Preparation-
The 20,734 images from 74 tumor cases were
recorded
The images from different cases can be used to
understand the diagnosis easily
Professional trained staff required for
efficient work.
30
S1047320318300804-main.pdf?
_tid=a27da700-7e16-423e-b435-
1861656b6fe2&acdnat=1532770818_9e6d
6243cbedae01e13d217defad7166
tumor
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Deep learning image selection algorithm
Tools:
Confocal imaging instrument instrument,
intraoperative data collection process
Applied Area:
Medical industry
Problem:
Confocal Laser Endomicroscopy (CLE) is the
advanced method in the brain tumor imaging.
But sometimes a large number of CLE images
can be distorted.
Goal:
The goal is to automatically select a diagnostic
image for examination.
ï‚· Fluorescein sodium (FNa)
ï‚· Rectified linear unit (ReLU)
ï‚· Normalization layer
ï‚· Activation layer
ï‚· Stochastic Gradient Descent (SGD)
ï‚· Convolutional layers
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Dataset Preparation-
The 20,734 images from 74 tumor cases were
recorded
The images from different cases can be used to
understand the diagnosis easily
Professional trained staff required for
efficient work.
30
2 Model Development-
After the splitting of the data, a patient related
five cross validation is developed for the model.
Different models are prepared to find the
optimal method
NA
3 Inter-observer Study-
The test is done on the different test datasets
developed.
The comparison is made between the human-
human and model-human inter-observer
agreements.
NA
4 Unsupervised histological feature
localisation-
The examination of neural activation is done at
two sites.
This detection feature could help the doctor in
treatment.
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Determine the sensitivity Reciever Operating characteristic (ROC) curve
Optimize the predictions Stochastic Gradient Descent (SGD)
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
The input in the
research is that a
model needs to be
prepared where the
image suitable for
diagnosis can be
He output is that the
deep learning model
is suggested so that
the diagnosable
images can
automatically be
CLE is used widely because of its ability to image
histopathological features of a tissue in real time.
The motion and the artifacts of blood that are there
in the images can be an obstruction in revealing
meaningful data from image. This necessity calls
for the selection of best image out of non-
diagnosable images to help the doctors.
The suggested model is at the beginning stage of
the evolution. Some techniques like combination
of different abstract features can be useful for
visual recognition. Thus these advanced features
can be added to the present system to increase the
efficiency and accuracy of the system.
31
After the splitting of the data, a patient related
five cross validation is developed for the model.
Different models are prepared to find the
optimal method
NA
3 Inter-observer Study-
The test is done on the different test datasets
developed.
The comparison is made between the human-
human and model-human inter-observer
agreements.
NA
4 Unsupervised histological feature
localisation-
The examination of neural activation is done at
two sites.
This detection feature could help the doctor in
treatment.
NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Determine the sensitivity Reciever Operating characteristic (ROC) curve
Optimize the predictions Stochastic Gradient Descent (SGD)
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
The input in the
research is that a
model needs to be
prepared where the
image suitable for
diagnosis can be
He output is that the
deep learning model
is suggested so that
the diagnosable
images can
automatically be
CLE is used widely because of its ability to image
histopathological features of a tissue in real time.
The motion and the artifacts of blood that are there
in the images can be an obstruction in revealing
meaningful data from image. This necessity calls
for the selection of best image out of non-
diagnosable images to help the doctors.
The suggested model is at the beginning stage of
the evolution. Some techniques like combination
of different abstract features can be useful for
visual recognition. Thus these advanced features
can be added to the present system to increase the
efficiency and accuracy of the system.
31
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automatically
selected.
detected.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The research suggests a deep learning algorithm
which will be able to automatically detect the
diagnostic CLE image for the brain tumor. The
datasets which are manually interpreted in-house
that are used here to test and train this approach.
The result of the research shows that both deep
fine-tuning and building a group of models can be
beneficial in enhancing the performance. Also their
combination could attain largest accuracy.
The proposed model in the research is also able to
confine some of the histological features of the
images to be diagnostic. The deep learning models
have helped achieving the accuracy in detection of
diagnostic images.
Diagram/Flowchart
32
selected.
detected.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The research suggests a deep learning algorithm
which will be able to automatically detect the
diagnostic CLE image for the brain tumor. The
datasets which are manually interpreted in-house
that are used here to test and train this approach.
The result of the research shows that both deep
fine-tuning and building a group of models can be
beneficial in enhancing the performance. Also their
combination could attain largest accuracy.
The proposed model in the research is also able to
confine some of the histological features of the
images to be diagnostic. The deep learning models
have helped achieving the accuracy in detection of
diagnostic images.
Diagram/Flowchart
32
Figure: A schematic diagram of interobserver study.
9
Reference in APA format that will be in Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P. A. (2018). VoxResNet: Deep voxelwise residual networks for
33
9
Reference in APA format that will be in Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P. A. (2018). VoxResNet: Deep voxelwise residual networks for
33
'Reference List' brain segmentation from 3D MR images. NeuroImage.
Citation that will be in the content (Chen, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S1053811917303348/1-s2.0-
S1053811917303348-main.pdf?
_tid=d044cee7-ffbf-4b9f-96c7-
2cbf09171f71&acdnat=1532774784_8a4c0
58e26ed5ffbd3008ee39f40f6fd
Q1 Brain segmentation, Multi-level contextual information,
Auto-context, Residual learning, Convolutional neural
network, Multi-modality, 3D deep learning
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Voxelwise Residual Network (VoxResNet)
Tools:
3D magnetic resonance (MR)
Applied Area:
Medical industry
Problem:
Segmentation of key brain tissues from 3d
images can be very beneficial for the medical
diagnosis. The manual segmentation is very
complicated as the anatomy of brain is hard to
understand.
Goal:
The goal is to propose a model which will help
in automated segmentation of the brain tissues.
ï‚· CNN
ï‚· C++
ï‚· Matlab
ï‚· Caffe library
ï‚· 3D magnetic resonance (MR)
ï‚· Brain segmentation
ï‚· 3D deep learning
The Process (Mechanism) of this Work; The process steps of the Technique/system
34
Citation that will be in the content (Chen, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S1053811917303348/1-s2.0-
S1053811917303348-main.pdf?
_tid=d044cee7-ffbf-4b9f-96c7-
2cbf09171f71&acdnat=1532774784_8a4c0
58e26ed5ffbd3008ee39f40f6fd
Q1 Brain segmentation, Multi-level contextual information,
Auto-context, Residual learning, Convolutional neural
network, Multi-modality, 3D deep learning
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Voxelwise Residual Network (VoxResNet)
Tools:
3D magnetic resonance (MR)
Applied Area:
Medical industry
Problem:
Segmentation of key brain tissues from 3d
images can be very beneficial for the medical
diagnosis. The manual segmentation is very
complicated as the anatomy of brain is hard to
understand.
Goal:
The goal is to propose a model which will help
in automated segmentation of the brain tissues.
ï‚· CNN
ï‚· C++
ï‚· Matlab
ï‚· Caffe library
ï‚· 3D magnetic resonance (MR)
ï‚· Brain segmentation
ï‚· 3D deep learning
The Process (Mechanism) of this Work; The process steps of the Technique/system
34
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Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Data Acquisition and Pre-Processing-
The datasets are acquired in this step
Evaluation of the algorithm is done NA
2 Deep Residual Learning-
The multiple layers of neurons are stacked
hierarchically to form a low, middle and high
feature based representation.
It can improve the judgment capability of a
particular network.
NA
3 VoxResNet for volumetric image
segmentation-
2D deep residual network is extended to 3D
deep residual network to get more representative
data
Volumetric feature representation learning is
strengthened.
NA
4 Multi-Modality and Auto-context
information Fusion-
The volumetric based data is completely
acquired by using multiple imaging modalities.
Various tissues can be examined robustly. Leaser accuracy
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
C++ & Matlab Caffe library
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
35
1 Data Acquisition and Pre-Processing-
The datasets are acquired in this step
Evaluation of the algorithm is done NA
2 Deep Residual Learning-
The multiple layers of neurons are stacked
hierarchically to form a low, middle and high
feature based representation.
It can improve the judgment capability of a
particular network.
NA
3 VoxResNet for volumetric image
segmentation-
2D deep residual network is extended to 3D
deep residual network to get more representative
data
Volumetric feature representation learning is
strengthened.
NA
4 Multi-Modality and Auto-context
information Fusion-
The volumetric based data is completely
acquired by using multiple imaging modalities.
Various tissues can be examined robustly. Leaser accuracy
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
C++ & Matlab Caffe library
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
35
Input (Data) Output (View)
The input of this
research is to suggest
a model that will
automatically do the
segmentation of the
3D medical images.
The output that can be
obtained from this
research is that the
deep learning model
with a training
module is proposed
which can help in
auto-segmentation of
3D medical images.
The 3D magnetic resonance images can greatly
affect the diagnosis of the medical
neurodegenerative disease. The manual
segmentation of the 3D images can consume a lot
of time and it is also an extremely laborious work.
Moreover, manual segmentation is prone to errors.
Therefore, automated segmentation is highly
required in the medical industry.
The use of the current model is limited to some
parts of the diagnosis which makes it a limitation
of the model. The automated brain structure
segmentation can be offered to various fields like
neuro-imaging and neuroscience studies where it
is essential to have accurate segmentation.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The research has proposed a model based on
VoxResNet which is a 3D residual network used
for automated segmentation of the brain structure
from 3D MR images. Various analyses have been
done on the capabilities in automated
segmentation. This model can also help the
advancement in brain structure segmentation
(Chen, et. al., 2018).
The method proposed in the research has extended
the 2D residual network to the 3D residual network
to solve the issues that were faced during the
segmentation of volumetric data using a deeper
network than the other models. Multi-modal and
Multi-level contextual based information were
carefully integrated in the end-to-end based
network to increase performance.
The additional features of the proposed model are
the addition of auto-context version of VoxResNet
which can enhance its performance by integrating
low-level appearance based information, implicit
shape based information and the high-level
context. Various experiments and analysis proves
that the proposed model is one of the efficient
models proposed recently.
Diagram/Flowchart
36
The input of this
research is to suggest
a model that will
automatically do the
segmentation of the
3D medical images.
The output that can be
obtained from this
research is that the
deep learning model
with a training
module is proposed
which can help in
auto-segmentation of
3D medical images.
The 3D magnetic resonance images can greatly
affect the diagnosis of the medical
neurodegenerative disease. The manual
segmentation of the 3D images can consume a lot
of time and it is also an extremely laborious work.
Moreover, manual segmentation is prone to errors.
Therefore, automated segmentation is highly
required in the medical industry.
The use of the current model is limited to some
parts of the diagnosis which makes it a limitation
of the model. The automated brain structure
segmentation can be offered to various fields like
neuro-imaging and neuroscience studies where it
is essential to have accurate segmentation.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The research has proposed a model based on
VoxResNet which is a 3D residual network used
for automated segmentation of the brain structure
from 3D MR images. Various analyses have been
done on the capabilities in automated
segmentation. This model can also help the
advancement in brain structure segmentation
(Chen, et. al., 2018).
The method proposed in the research has extended
the 2D residual network to the 3D residual network
to solve the issues that were faced during the
segmentation of volumetric data using a deeper
network than the other models. Multi-modal and
Multi-level contextual based information were
carefully integrated in the end-to-end based
network to increase performance.
The additional features of the proposed model are
the addition of auto-context version of VoxResNet
which can enhance its performance by integrating
low-level appearance based information, implicit
shape based information and the high-level
context. Various experiments and analysis proves
that the proposed model is one of the efficient
models proposed recently.
Diagram/Flowchart
36
Figure: An overview of our proposed framework for integrating auto-context with multi-modality information
37
37
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10
Reference in APA format that will be in
'Reference List'
Raju, A. R., Suresh, P., & Rao, R. R. (2018). Bayesian HCS-based multi-SVNN: A classification approach for
brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybernetics and Biomedical
Engineering.
Citation that will be in the content (Raju, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S0208521617303923/1-s2.0-
S0208521617303923-main.pdf?
_tid=dff04457-be6b-4b96-9336-
6a2381155c83&acdnat=1532776500_f062
299f5f65b7d5eb23241808802155
Q3 Brain tumor classification, Bayesian fuzzy clustering, MRI
image, Support vector neural network, Brain tumor
segmentation
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Bayesian HCS-based multi SVNN
classifier
Tools:
Harmony-Crow Search (HCS) algorithm,
BRATS database, machine learning
classification techniques
Applied Area:
Medical industry
Problem:
The segmentation of the brain structure is
advancement in technology in medical field
but the automatic segmentation is lacking to
identify which part of brain is tumorous and
which part is not.
Goal:
The goal is to automatically classify the
tumorous area and the non-tumorous area of
the brain using a model.
ï‚· BRATS database
ï‚· Fuzzy C-means clustering
ï‚· Multi-SVNN classifier
ï‚· Harmony Search Algorithm
ï‚· Crow Search Algorithm (CSA)
ï‚· Bayesian fuzzy clustering (BFC)
38
Reference in APA format that will be in
'Reference List'
Raju, A. R., Suresh, P., & Rao, R. R. (2018). Bayesian HCS-based multi-SVNN: A classification approach for
brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybernetics and Biomedical
Engineering.
Citation that will be in the content (Raju, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://ac-els-cdn-
com.ezproxy.csu.edu.au/
S0208521617303923/1-s2.0-
S0208521617303923-main.pdf?
_tid=dff04457-be6b-4b96-9336-
6a2381155c83&acdnat=1532776500_f062
299f5f65b7d5eb23241808802155
Q3 Brain tumor classification, Bayesian fuzzy clustering, MRI
image, Support vector neural network, Brain tumor
segmentation
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Bayesian HCS-based multi SVNN
classifier
Tools:
Harmony-Crow Search (HCS) algorithm,
BRATS database, machine learning
classification techniques
Applied Area:
Medical industry
Problem:
The segmentation of the brain structure is
advancement in technology in medical field
but the automatic segmentation is lacking to
identify which part of brain is tumorous and
which part is not.
Goal:
The goal is to automatically classify the
tumorous area and the non-tumorous area of
the brain using a model.
ï‚· BRATS database
ï‚· Fuzzy C-means clustering
ï‚· Multi-SVNN classifier
ï‚· Harmony Search Algorithm
ï‚· Crow Search Algorithm (CSA)
ï‚· Bayesian fuzzy clustering (BFC)
38
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Segmentation using the Bayesian Fuzzy
Clustering-
Identification of core and the edema regions.
Flexibility and the capability of the algorithm
are proved.
Require high accuracy
2 Feature Extraction-
Wavelet transform, scattering transform and
theoretical data is used to extract features.
Features are extracted for segmentation. NA
3 Detection of tumour and tumour level-
The classification is done on the basis of
features extracted
The classification of the tumour is done NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
BFC model Fuzzy data
Semi-automatic methods User interaction
Optimization algorithm Multi-SVNN classifier
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
The input of the The output of this
In the advanced world, the MRI is not good enough
for detecting and segmenting the brain structure for
tumor. But using MRI images the segmentation can
The performance of the proposed framework is
very good based on the results and analysis done
on the model. But the limitation of this model can
39
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Segmentation using the Bayesian Fuzzy
Clustering-
Identification of core and the edema regions.
Flexibility and the capability of the algorithm
are proved.
Require high accuracy
2 Feature Extraction-
Wavelet transform, scattering transform and
theoretical data is used to extract features.
Features are extracted for segmentation. NA
3 Detection of tumour and tumour level-
The classification is done on the basis of
features extracted
The classification of the tumour is done NA
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
BFC model Fuzzy data
Semi-automatic methods User interaction
Optimization algorithm Multi-SVNN classifier
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
The input of the The output of this
In the advanced world, the MRI is not good enough
for detecting and segmenting the brain structure for
tumor. But using MRI images the segmentation can
The performance of the proposed framework is
very good based on the results and analysis done
on the model. But the limitation of this model can
39
research is that a
model can be
proposed which will
automatically classify
the tumour region of
the brain and the level
of tumour.
research is that the
model is developed
which can
automatically look for
tumour region in brain
and also it can classify
the level of tumour.
be done. In the medical image processing,
segmentation plays an important role for diagnosis
and the analysis of images.
be that it is still in evolution field and more
accuracy and precision is needed in the model.
Moreover, additional datasets can be added to the
system so that the performance of the system can
be enhanced.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The proposed model of Bayesian HCS-based
multi SVNN classifier is used to automatically
segment the brain structure and classify the level
of tumor based on the MRI images. The proposed
model is proved to be much accurate then its
counterparts from various experiments and
evaluation done on the system. The system is
much more efficient when compared with manual
process (Raju, et. al., 2018).
The suggested model of the HCS algorithm is
integrated from CSA and the harmony search
optimization that engages itself into the calculation
of optimal weights to multi-SVNN. The proposed
system can easily and automatically classify the
tumor level of the brain using these algorithms.
The highly robust features of the system that are
obtained from the slices are employed to detect the
existence of non-tumor and edema tumors that
shows the clear-cut presence of abnormalities in
brain. The analysis done with the help of BRATS
database says that tumor classification obtains
accuracy of 0.93, sensitivity of 0.9690 and the
specificity of 0.9929.
Diagram/Flowchart
40
model can be
proposed which will
automatically classify
the tumour region of
the brain and the level
of tumour.
research is that the
model is developed
which can
automatically look for
tumour region in brain
and also it can classify
the level of tumour.
be done. In the medical image processing,
segmentation plays an important role for diagnosis
and the analysis of images.
be that it is still in evolution field and more
accuracy and precision is needed in the model.
Moreover, additional datasets can be added to the
system so that the performance of the system can
be enhanced.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The proposed model of Bayesian HCS-based
multi SVNN classifier is used to automatically
segment the brain structure and classify the level
of tumor based on the MRI images. The proposed
model is proved to be much accurate then its
counterparts from various experiments and
evaluation done on the system. The system is
much more efficient when compared with manual
process (Raju, et. al., 2018).
The suggested model of the HCS algorithm is
integrated from CSA and the harmony search
optimization that engages itself into the calculation
of optimal weights to multi-SVNN. The proposed
system can easily and automatically classify the
tumor level of the brain using these algorithms.
The highly robust features of the system that are
obtained from the slices are employed to detect the
existence of non-tumor and edema tumors that
shows the clear-cut presence of abnormalities in
brain. The analysis done with the help of BRATS
database says that tumor classification obtains
accuracy of 0.93, sensitivity of 0.9690 and the
specificity of 0.9929.
Diagram/Flowchart
40
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Figure- Proposed HCS-based multi-SVNN for brain tumor segmentation and classification
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