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Pancreas Segmentation

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Kun Zhan

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Pancreas Segmentation

  1. 1. MICCAI 17 CVPR 18 MICCAI 18 Pancreas Segmentation Kun Zhan UTS Building 11 15 July, 2018 Kun Zhan Pancreas Segmentation
  2. 2. MICCAI 17 CVPR 18 MICCAI 18 Content I 1 MICCAI 17 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans 2 CVPR 18 Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation 3 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net Kun Zhan Pancreas Segmentation
  3. 3. MICCAI 17 CVPR 18 MICCAI 18 Pancreatic Cancer 1 Pancreatic cancer is a major killer of human being 12 out of 100,000 people have pancreatic cancer 330,000 new cases globally in the year of 2012 (7th most) 2 Even in all types of cancer, it belongs to the most dangerous and fatal ones Extremely difficult to diagnose in its early stage In the time of diagnosis, the cancer has often spread to other organs Most often, nothing can be done to cure the patients (5-year survival rate: 7.7%) The rate of diagnosis is almost the same as the rate of death Kun Zhan Pancreas Segmentation
  4. 4. MICCAI 17 CVPR 18 MICCAI 18 The NIH Dataset 82 CT samples collected from healthy people A volume is of size 512 × 512 × L, L ∈ [181, 466] varies from sample to sample, and the slice thickness varies between 1.5mm and 2.5mm Slice-by-slice (in the axial view) annotated by a medical student, and verified (modified) by an experienced radiologist Kun Zhan Pancreas Segmentation
  5. 5. MICCAI 17 CVPR 18 MICCAI 18 Occupy a very small fraction (e.g., < 0.5%) of the CT volume. Kun Zhan Pancreas Segmentation
  6. 6. MICCAI 17 CVPR 18 MICCAI 18 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Outline 1 MICCAI 17 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans 2 CVPR 18 Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation 3 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net Kun Zhan Pancreas Segmentation
  7. 7. MICCAI 17 CVPR 18 MICCAI 18 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Outline 1 MICCAI 17 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans 2 CVPR 18 Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation 3 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net Kun Zhan Pancreas Segmentation
  8. 8. MICCAI 17 CVPR 18 MICCAI 18 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Model. (pre-trained “fcn8s-heavy-pascal.caffemodel”) VGG-16 FCN-8s Kun Zhan Pancreas Segmentation
  9. 9. MICCAI 17 CVPR 18 MICCAI 18 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Loss Function DSC Loss Function: Dice Sφrensen Coeffcient = 1 − 2× i ziyi i zi+ i yi Loss Function: L = 1 − 2× i ziyi+ i zi+ i yi+ The gradient computation is straightforward: ∂L ∂zj = − 2yj( i zi+ i yi+ )−2 i ziyi− ( i zi+ i yi+ )2 Kun Zhan Pancreas Segmentation
  10. 10. MICCAI 17 CVPR 18 MICCAI 18 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Segmentation results with different input regions Red, green, and yellow indicate the prediction, ground-truth, and overlapped pixels, respectively. Kun Zhan Pancreas Segmentation
  11. 11. MICCAI 17 CVPR 18 MICCAI 18 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scheme Illustration of the testing process. Only one iteration is shown here. In practice, there are at most 10 iterations. Kun Zhan Pancreas Segmentation
  12. 12. MICCAI 17 CVPR 18 MICCAI 18 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT The floodfill algorithm is used to detect the connecting components. Kun Zhan Pancreas Segmentation
  13. 13. MICCAI 17 CVPR 18 MICCAI 18 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Results Segmentation accuracy reported by different approaches. Kun Zhan Pancreas Segmentation
  14. 14. MICCAI 17 CVPR 18 MICCAI 18 Recurrent Saliency Transformation Network: Incorporating Multi- Outline 1 MICCAI 17 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans 2 CVPR 18 Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation 3 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net Kun Zhan Pancreas Segmentation
  15. 15. MICCAI 17 CVPR 18 MICCAI 18 Recurrent Saliency Transformation Network: Incorporating Multi- Outline 1 MICCAI 17 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans 2 CVPR 18 Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation 3 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net Kun Zhan Pancreas Segmentation
  16. 16. MICCAI 17 CVPR 18 MICCAI 18 Recurrent Saliency Transformation Network: Incorporating Multi- Motivation Since “MICCAI 2017” is inconsistency between its training and testing strategies, we propose a Recurrent Saliency Transformation Network with two advantages: 1 In the training phase, the coarse-scaled and fine-scaled networks are optimized jointly, so that the segmentation ability of each of them gets improved. 2 In the testing phase, the segmentation mask of each iteration is preserved and propagated throughout iterations, enabling multi-stage visual cues to be incorporated towards more accurate segmentation. ‘To the best of our knowledge, this idea was not studied in the computer vision community, as it requires making use of some special properties of CT scans.’ Kun Zhan Pancreas Segmentation
  17. 17. MICCAI 17 CVPR 18 MICCAI 18 Recurrent Saliency Transformation Network: Incorporating Multi- A failure case of “MICCAI 2017” Kun Zhan Pancreas Segmentation
  18. 18. MICCAI 17 CVPR 18 MICCAI 18 Recurrent Saliency Transformation Network: Incorporating Multi- RSTN Scheme Kun Zhan Pancreas Segmentation
  19. 19. MICCAI 17 CVPR 18 MICCAI 18 Recurrent Saliency Transformation Network: Incorporating Multi- RSTN Results Kun Zhan Pancreas Segmentation
  20. 20. MICCAI 17 CVPR 18 MICCAI 18 Recurrent Saliency Transformation Network: Incorporating Multi- RSTN Results Kun Zhan Pancreas Segmentation
  21. 21. MICCAI 17 CVPR 18 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Outline 1 MICCAI 17 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans 2 CVPR 18 Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation 3 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net Kun Zhan Pancreas Segmentation
  22. 22. MICCAI 17 CVPR 18 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Outline 1 MICCAI 17 A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans 2 CVPR 18 Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation 3 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net Kun Zhan Pancreas Segmentation
  23. 23. MICCAI 17 CVPR 18 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Highlights 1 We present an alternative framework, which trains 2D networks on different view points for segmentation, and builds a 3D Volumetric Fusion Net (VFN) to fuse the 2D segmentation results. 2 We train and test the segmentation and fusion modules individually, and propose a novel strategy, named cross-cross-augmentation, to make full use of the limited training data. cross-cross-augmentation: Suppose we split data into K folds for cross-validation, and the k1-th fold is left for testing. For all k2 = k1, we train 2D segmentation models on the folds in {1, . . . , K} {k1, k2}, and test on the k2-th fold to generate training data for the VFN. Kun Zhan Pancreas Segmentation
  24. 24. MICCAI 17 CVPR 18 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with VFN Scheme It has three down-sampling stages and three up-sampling stages. Each down-sampling stage is composed of two 3 × 3 × 3 convolutional layers and a 2 × 2 × 2 max-pooling layer with a stride of 2, and each up-sampling stage is implemented by a single 4 × 4 × 4 deconvolutional layer with a stride of 2. Kun Zhan Pancreas Segmentation
  25. 25. MICCAI 17 CVPR 18 MICCAI 18 Bridging the Gap Between 2D and 3D Organ Segmentation with Results Comparison of segmentation accuracy Kun Zhan Pancreas Segmentation

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