I N F O R M AT I O N T E C H N O L O G YE L E C T R I C A L E N G I N E E R I N G A N DFA C U LT Y O F2019 Tumor Segmentation Using CNN j 1/27
Brain Tumor Segmentation Using Convolutional
IEEE TRANSACTIONS ON MEDICAL IMAGING,VOL.35,NO.5,MAY2016
S
 ergio Pereira, Adriano Pinto, Victor Alves, and Carlos A.
Silva
Student presentation of the Scientifc working class
at, Magdeburg
Created and presented by: Divya Thomas
12 November,2019
I N F O R M AT I O N T E C H N O L O G YE L E C T R I C A L E N G I N E E R I N G A N DFA C U LT Y O F2019 Tumor Segmentation Using CNN j 2/27
1. Introduction
2. Proposed methodology
3. Experimental setup
4. Results and Discussions
5. Literature
2019 Tumor Segmentation Using CNN j 3/27
2014).
2019 Tumor Segmentation Using CNN
C onvolutionalN euralN etwork (C N N )
CNN consists of an input, output
Jakab, et al. 2014). Neural networking (Menze,
2019 Tumor Segmentation Using CNN
Brain and spinal cord
Graded as Low Grade Gliomas(LGG)
and High Grade Gliomas(HGG). Gliomas (Pereira et al. 2016)
2019 Tumor Segmentation Using CNN
Manual segmentation(Forstmann, Keuken, and Alkemade
2015).
Semi-automatic or automatic methods(Forstmann, Keuken, and
Probabilistic atlases(Menze, Van Leemput, et al. 2010).
Tumor growth models(Menze, Van Leemput, et al. 2010).
From Voxel Distribution(Menze, Van Leemput, et al. 2010).
Using classifers like support vector machines / Random
Forests/ Spatially Adaptive RF(Bauer, Nolte, and Reyes 2011).
Deep learning(Bauer, Nolte, and Reyes 2011).
2019 Tumor Segmentation Using CNN
Overview of proposed method (Pereira et al. 2016)
2019 Tumor Segmentation Using CNN
Apply intensity
Compute mean
Normalize the patches Neural Bias feld distortion (Ny

ul,
Udupa, and Zhang 2000) Intensity normalization (Ny

2019 Tumor Segmentation Using CNN
(Krizhevsky, Sutskever, and G. E. Hinton 2012) Initialization :
Done to make sure that the input values fall inside a desired
Xavier initialization is used (Glorot and Yoshua Bengio 2010).
Activation Function :
Act as a gate between current layer and next layer.
Rectifer linear units are used(Krizhevsky, Sutskever, and
Pooling :
Used to reduce the total computational load and represent
Max pooling is used(LeCun, Bengio, and G. Hinton 2015).
2019 Tumor Segmentation Using CNN
Regularization :
To generalize the model which makes less prone to
overftting(Krizhevsky, Sutskever, and G. E. Hinton 2012).
Remove nodes with some probability, Dropout(Srivastava et al.
2014) is used.
Data Augmentation :
Helps to artifcially expand the training size(Krizhevsky,
By rotating the original data by 90 degree.
Loss Function :
Indicates how well an algorithm models the data set.
Categorical cross-entropy is used.
2019 Tumor Segmentation Using CNN
Loss Function :
For each data value specifed category is used.
It compare the predicted value with the true distributions, where
(c 0
j;k
)
(1)
where c’ is probabilistic predictions and c, the target.
Training :
Stochastic Gradient Descent, which actually replaces the actual
gradient and is used to minimize the lose function.
At region of low curvature Nesterov’s accelerated Momentum is
2019 Tumor Segmentation Using CNN
Impose Volumetric Constrains-di erential and remove tissue clusters
and tumor cells Clusters miss-interpreted as tumor cells (Ny

2019 Tumor Segmentation Using CNN
BRATS 2013 and 2015 databases are used for
validation(Menze, Jakab, et al. 2014).
4 MRI sequences are available for each patient .
BRATS 2013 have three data sets: Training, Leader-board and
BRATS 2015 have the Training set of 220 HGG and 54 LGG.
2019 Tumor Segmentation Using CNN
Tumor is approached as a multi-class classifcation problem.
For training the Neural network 450,000 HGG and 335,000 LGG
CNN is developed using Theano(Bastien et al. 2012) and
2019 Tumor Segmentation Using CNN
Considered 3 metrices: 1 Dice Similarity Coe cient (DSC):measures the overlap between
the manual and the automatic segmentation
+ 2T P +F N (2)2 Positive Predictive Value (PPV): measure of the amount of FP
+F P (3)3 Sensitivity: evaluate the number of TP and FN detections
+F N (4)
where TP, FP and FN are the numbers of true positive, false
positive and false negative detections, respectively.
2019 Tumor Segmentation Using CNN
Validation of Key Components. 1 Pre-processing
2 Data Augmentation
3 Activation Function
4 Deeper Architectures/Small Kernels
Patch Extraction Plane.
Global Validation.
2019 Tumor Segmentation Using CNN
Mean gain is calculated by subtracting the metrics of alternating
Leader-board data set. Diamonds indicate mean(Pereira et al. 2016)
.
2019 Tumor Segmentation Using CNN
Proposed pre-processing increased the detection of the complete
Challenge data set. Diamonds gives the mean (Pereira et al. 2016)
2019 Tumor Segmentation Using CNN
Over-segmentation of tumor gives these images. In HGG variant 2
classifed some non-enhanced tumor inside enhancing ring and in
LGG even in big Kernals couldn’t give total enhancment.
HGG Segmentation with cross-validation showing e ect of each component of the
proposed methods. Di erent colours represent tumor class. (Pereira et al. 2016)
.
2019 Tumor Segmentation Using CNN
LGG Segmentation with cross-validation showing e ect of each component of the
proposed methods. Di erent colours represent tumor class. (Pereira et al. 2016)
.
2019 Tumor Segmentation Using CNN
Fig shows T1,T1c,T2,FLAIR and the segmentation. Each colour
HGG and LGG Segmentation in the Leader-board data set (Pereira et al. 2016)
.
2019 Tumor Segmentation Using CNN
A patient with 2 tumor were correctly detected and segmented from
Segmentation in Challenge data set. From left: T1,T1c,T2,FLAIR and segmentation.
(Pereira et al. 2016)
.
2019 Tumor Segmentation Using CNN
Proposed method helps to reduce the computation time as well as
increase image enhancement comparing with other methods.
No limitations or disadvantages regarding method are discussed in
2019 Tumor Segmentation Using CNN
2019 Tumor Segmentation Using CNN
ed
eric et al. (2012). Theano: new features and speed
improvements”. In: arXiv preprint arXiv:1211.5590 .Bauer, Stefan, Lutz-P Nolte, and Mauricio Reyes (2011). Fully
automatic segmentation of brain tumor images using support
vector machine classifcation in combination with hierarchical
conditional random feld regularization”. In: international
conference on medical image computing and computer-assisted
intervention . Springer, pp. 354{361.Dieleman, S et al. (2015).
10.5281/zenodo. 27878 .Forstmann, Birte U, Max C Keuken, and Anneke Alkemade (2015).
An introduction to human brain anatomy”. In: An Introduction
to Model-Based Cognitive Neuroscience . Springer, pp. 71{89.
2019 Tumor Segmentation Using CNN
Glorot, Xavier and Yoshua Bengio (2010). Understanding the
di culty of training deep feedforward neural networks”. In:
Proceedings of the thirteenth international conference on artifcial
intelligence and statistics , pp. 249{256.Krizhevsky, Alex, Ilya Sutskever, and Geo rey E Hinton (2012).
Imagenet classifcation with deep convolutional neural networks”.In: Advances in neural information processing systems ,
pp. 1097{1105. LeCun, Y, Y Bengio, and G Hinton (2015). Deep learning. nature
521″. In: Menze, Bjoern H, Andras Jakab, et al. (2014). The multimodal
brain tumor image segmentation benchmark (BRATS)”. In: IEEE
transactions on medical imaging 34.10, pp. 1993{2024.
2019 Tumor Segmentation Using CNN
Menze, Bjoern H, Koen Van Leemput, et al. (2010). A generative
model for brain tumor segmentation in multi-modal images”. In:
International Conference on Medical Image Computing and
Computer-Assisted Intervention . Springer, pp. 151{159.Ny

aszl

o G, Jayaram K Udupa, and Xuan Zhang (2000). New
variants of a method of MRI scale standardization”. In: IEEE
transactions on medical imaging 19.2, pp. 143{150.Pereira, S
ergio et al. (2016). Brain tumor segmentation using
convolutional neural networks in MRI images”. In: IEEE
transactions on medical imaging 35.5, pp. 1240{1251.Srivastava, Nitish et al. (2014). Dropout: a simple way to prevent
neural networks from overftting”. In: The journal of machine
learning research 15.1, pp. 1929{1958.
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