Paper title : Structure Boundary Preserving Segmentation
for Medical Image with Ambiguous Boundary (CVPR2020) Paper link : https://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_Structure_Boundary_Preserving_Segmentation_for_Medical_Image_With_Ambiguous_Boundary_CVPR_2020_paper.pdf

1. Structure Boundary Preserving Segmentation
for Medical Image with Ambiguous Boundary
Dong Min Choi
Yonsei University Severance Hospital CCIDS

2. Introduction
• Two critical problems of medical image segmentation
- i) ambiguity of structure boundary
- ii) uncertainty of the segmented region without specialized
medical knowledge
• Propose a novel image segmentation method to tackle these
problems
- a novel structure boundary preserving segmentation framework

3. Introduction

4. Proposed Architecture
Overall Architecture

5. Proposed Architecture
1. BPB (Boundary Preserving Block)
Overall Architecture

6. Proposed Architecture
1. BPB (Boundary Preserving Block)
2. SBE (Shape
Boundary-aware Evaluator)
Overall Architecture

7. Proposed Architecture
1. BPB (Boundary Preserving Block)
2. SBE (Shape
Boundary-aware Evaluator)
Overall Architecture

8. Proposed Architecture
Boundary Key-point Selection Module
- Generates the boundary key points that
1) best fit the GT segmentation map
2) represent structure boundary of the
target region
- Steps
1) Using canny edge detection algo,
obtain the boundary from GT
2) Select n points randomly on the
boundary
3) Construct the boundary region by
connecting the n points
4) Select the boundary points with the
highest value of IOU after T-th iterationImplement details : n=6, T=40,000

9. Proposed Architecture
Boundary Key-point Selection Module
- Generates the boundary key points that
1) best fit the GT segmentation map
2) represent structure boundary of the
target region
- Steps
1) Using canny edge detection algo,
obtain the boundary from GT
2) Select n points randomly on the
boundary
3) Construct the boundary region by
connecting the n points
4) Select the boundary points with the
highest value of IOU after T-th iterationImplement details : n=6, T=40,000

10. Proposed Architecture
BPB (Boundary Preserving Block)
- Enhance boundary information in
feature-map level
- Boundary Point Map Generator
* Input : feature map
* Output : Boundary Key-point map
* Various receptive fields with
( : dilation ratio and filter)
* Loss function :
- Boundary Enhanced Feature :
fi
Mi
ds
r
ds
r r s × s
LMap

11. Proposed Architecture
SBE (Shape Boundary-aware Evaluator)
- A novel structure boundary information-based discrimination network
* Input : Boundary keypoint map and segmentation image ( and )
* Concat and the given segmentation image across the channel-wise
* Output : whether the segmentation results are consistent with the or not
- Used in training stage only
MGT SGT
̂SPred
MGT
MGT

12. Loss Function
< The Segmentation Training Loss >
*
*
*
< The Adversarial SBE Loss >

13. Result

14. Result

15. Result
Qualitative Results : PH2 + ISBI 2016 Challenge dataset

16. Result
Qualitative Results : TVUS Challenge dataset

17. Result
- Using 6 key points shows the best performance
- But, this number of key points is not optimal for all objects
- The more complex structures are, the more key points and iterations are needed

18. Conclusion
• A novel fully automatic segmentation framework for medical
image segmentation with an ambiguity boundary
• Using BPB and SBE, segmentation network further exploits the
structure boundary
• The proposed framework can easily be integrated into various
segmentation networks

19. Thank you