I reviewed 3 papers at 'SNU TF Study Group' in Korea. 3 papers tried to solve segmentation problems in medical images with Deep Learning. Deep Learning 을 이용하여 의료 영상에서 Segmentation 문제를 풀고자 한 3가지 논문을 리뷰하였습니다. :)

1. 2 4 O c t . 2 0 1 7
Segmentation Problems
in Medical Images
Jimin Lee
Radiological Physics Laboratory,
Seoul National University
SNU TF 스터디 모임

2. Contents
2
1. Spinal cord gray matter segmentation using deep dilated convolutions
( https://arxiv.org/abs/1710.01269 )
2. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Liver Tumor
Segmentation from CT Volumes ( https://arxiv.org/abs/1709.07330 )
3. Automatic Myocardial Segmentation by Using A Deep Learning Network in
Cardiac MRI ( https://arxiv.org/abs/1708.07452 )

3. Spinal cord gray matter segmentation
using deep dilated convolutions
October 4, 2017
* Article under submission to Nature Scientific Reports.

4. 1. Introduction
4
 Purpose : To devise an end-to-end fully automated human spinal cord gray matter
segmentation method using Deep Learning
 Gray matter (GM) tissue changes in spinal cord (SC)
• It is linked to neurological disorders.
• SCGM atrophy (위축) is a relevant biomarker for predicting disability in amyotrophic
lateral sclerosis (루게릭병).
 The fully-automated segmentation is very useful for longitudinal studies.
• The delineation of gray matter is very time-consuming.

5. 1. Introduction
5
 Accurate segmentation of the GM is still a remaining challenge.
• Inconsistent surrounding tissue intensities
• Pathology-induced changes in the image contrast
• Differences in MRI acquisition protocols
• Lack of standardized data sets
• Different pixel sizes
• Image artifacts
⋮
MRI samples from different centers → Variability

6. 2. Methods and Materials
6
 Dilated convolution
https://github.com/vdumoulin/conv_arithmetic
3x3 Kenel with dilation rate 2

7. 2. Methods and Materials
7
 Proposed method
2 layers each

8. 2. Methods and Materials
8
 Loss : DSC (Dice Similarity Coefficient)
 Data augmentation : rotation, shifting, scaling, flipping, noise, elastic deformation
p : Predictions, r : Gold standard
* 𝜖 term is used to ensure the loss stability by avoiding the numerical issues.

9. 2. Methods and Materials
9
 Data sets
(1) Spinal Cord Gray Matter Challenge
• 80 healthy subjects (20 subjects from 4 centers)
• 3 different MRI systems (Philips Achieva, Siemens Trio, Siemens Skyra)
• Training set : 40 subjects, Test set : 40 subjects

10. 2. Methods and Materials
10
 Data sets
(2) Ex vivo high-resolution spinal cord
• An entire human spinal cord
• 7T horizontal-bore small animal MRI system
• 4676 axial slices with 100 𝜇𝑚 resolution
• (120 hours to take..!)

11. 2. Methods and Materials
11
 Training Protocol (Spinal Cord Gray Matter Challenge dataset)
• Resampling and cropping : 0.25 x 0.25 mm (resampled) / 200 x 200 pixels (cropped)
• Normalization : mean centering, SD normalization
• Train/validation set split : 32 subjects / 8 subjects
• Batch size = 11 (samples), 1000 epochs (32 batches at each epoch)
• Optimization : Adam optimizer (Learning rate : 0.001)
• Batch Normalization : momentum ∅ = 0.01
• Dropout : rate of 0.4
• Learning rate scheduling (𝜂 𝑡0
: initial learning rate, N : the number of epochs, p = 0.9)

12. 3. Results
12
 Spinal Cord Gray Matter Challenge

13. 3. Results
13
 Spinal Cord Gray Matter Challenge

14. 3. Results
14
 Ex vivo high-resolution spinal cord
 U-Net (비교용)
• 14 layers
• 3x3 2D convolution filters
• Same training protocol & loss

15. 3. Results
15
 Ex vivo high-resolution spinal cord

16. 4. Discussion
16
 They devised a simple, but efficient and end-to-end method.
 Compared to U-Net, proposed method provides better results in many metrics.
 A major parameter reduction (more than 6 times)
 IU : Intersection over Union

17. H-DenseUNet: Hybrid Densely
Connected UNet for Liver and Liver
Tumor Segmentation from CT Volumes
September 21, 2017

18. 1. Introduction
18
 Purpose : Liver and liver tumor segmentation in contrast-enhanced 3D abdominal CT scans
 Liver and liver tumor segmentation can assist
doctors in making accurate hepatocellular
carcinoma evaluation and treatment planning.
 Automatic segmentation is a very challenging task.
• Tumor : Various size, shape, location and
numbers within one patient
• Not clear boundaries
• CT voxel spacing ranges from 0.45mm to
6.0mm

19. 2. Method
19
 Proposed method : A novel hybrid densely connected UNet (H-DenseUNet)
 U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation ( https://arxiv.org/abs/1505.04597 )
To aggregate
semantic information
To recover the spatial
information with the help of
shortcut connections

20. 2. Method
20
 Proposed method : A novel hybrid densely connected UNet (H-DenseUNet)
 2D DenseUNet

21. 2. Method
21
 Proposed method : A novel hybrid densely connected UNet (H-DenseUNet)
 DenseUNet

22. 2. Method
22
 Proposed method : A novel hybrid densely connected UNet (H-DenseUNet)
 H-DenseUNet : 2D DenseUNet + 3D DenseUNet

23. 3. Experiment and Results
23
 Data set
• MICCAI 2017 LiTS (Liver Tumor Segmentation) Challenge
• Contrast-enhanced 3D abdominal CT scans
• 131 for training, 70 for testing
 Evaluation Metrics
• Dice per case score : An average Dice per volume score
• Dice global score : Dice score evaluated by combining all datasets into one

24. 3. Experiment and Results
24
 Results

25. 3. Experiment and Results
25
 Results

26. Automatic Myocardial Segmentation
by Using A Deep Learning Network
in Cardiac MRI
August 24, 2017

27. 1. U-Net
27
 Purpose : Myocardial (심근) segmentation in Cardiac MRI
 Myocardial motion is useful in the evaluation of regional cardiac functions.

28. 2. Results
28

29. 2. Results
29