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A pixel-to-pixel segmentation of DILD
without masks
using CNN and Perlin noise
2016.11 njkim@jamonglab.com
Objectives
● Segmenting and labeling regional patterns in
DILD(Diffuse Interstitial Lung Disease) HRCT
images.
From : Youn...
Challenges
● Small dataset
○ only 547 ROI ( 20x20 bounding box ) patches
● No human mask label
○ Extremely expensive
Dataset
Dataset
Dataset
Dataset
Traditional approach
● Superpixel approach
Traditional approach
● Superpixel result - factor 0.25
Traditional approach
● Superpixel result - factor 2
Traditional approach
● Superpixel result - factor 4
Traditional approach
● Superpixel result - factor 9
Traditional approach
● Superpixel accuracy
Traditional approach
● Superpixel limitation
○ deterministic and strong assumption
( Similarity of neighboring pixels )
New approach
● Deep learning pixel-to-pixel segmentation.
○ Hand labelled mask is needed.
○ Let’s generate it !
From : Ra ...
Mask generation
● A naive approach → Failed.
○ Because the neural network have learned deterministic
patterns instead of l...
Mask generation
● Ken Perlin, “An image Synthesizer”, 1985
○ natural appearing textures
○ gradient based fractal noise
○ h...
Mask generation
● One random Perlin noise ( simplex noise )
● two randomly selected ROI patches
ConsolidationGGO
Mask ROI ...
Mask generation
● 547 patches → Infinite patches ( O(1006xN
) )
Model architecture
● UNet + SWWAE architecture
○ Olaf et al, “U-Net: Convolutional Networks for Biomedical Image
Segmentat...
Model architecture
Skip connections
Deep learning approach
● pixel-to-pixel segmentation result
Deep learning approach
● pixel-to-pixel segmentation result
Deep learning approach
● pixel-to-pixel segmentation result
Deep learning approach
● pixel-to-pixel segmentation accuracy
High resolution segmentation
● 20 x 20 patches per 512 x 512 image
○ (512 - 20 + 1)2
→ Too expensive
High resolution segmentation
● Fully convolutional layer used
○ Various sized image input available
High resolution segmentation
● 200 x 80 grids
High resolution segmentation
● 500 x 20 grid ( Vertical grids )
High resolution segmentation
● 20 x 500 grid ( Horizontal grids )
High resolution segmentation
● Computation complexity
High resolution segmentation
● Results ( Hortz )
High resolution segmentation
● Results ( Vert )
High resolution segmentation
● Results ( Mix )
High resolution segmentation
● Comparison - Accuracy
High resolution segmentation
● Comparison - computation time
Our contributions
● A simple and practical pixel mask generation
method for DILD ROI dataset using Perlin noise.
○ No radi...
Thank you !!!
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A pixel to-pixel segmentation method of DILD without masks using CNN and perlin noise

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A pixel to-pixel segmentation method of diffuse interstitial lung disease images without human masks using deep convolution networks and perlin noise

Publicado en: Ingeniería
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A pixel to-pixel segmentation method of DILD without masks using CNN and perlin noise

  1. 1. A pixel-to-pixel segmentation of DILD without masks using CNN and Perlin noise 2016.11 njkim@jamonglab.com
  2. 2. Objectives ● Segmenting and labeling regional patterns in DILD(Diffuse Interstitial Lung Disease) HRCT images. From : Younjun Chang et al, “Fast and efficient lung disease classification using hierarchical one-against-all SVM and cost-sensitive feature selection”. 2012.
  3. 3. Challenges ● Small dataset ○ only 547 ROI ( 20x20 bounding box ) patches ● No human mask label ○ Extremely expensive
  4. 4. Dataset
  5. 5. Dataset
  6. 6. Dataset
  7. 7. Dataset
  8. 8. Traditional approach ● Superpixel approach
  9. 9. Traditional approach ● Superpixel result - factor 0.25
  10. 10. Traditional approach ● Superpixel result - factor 2
  11. 11. Traditional approach ● Superpixel result - factor 4
  12. 12. Traditional approach ● Superpixel result - factor 9
  13. 13. Traditional approach ● Superpixel accuracy
  14. 14. Traditional approach ● Superpixel limitation ○ deterministic and strong assumption ( Similarity of neighboring pixels )
  15. 15. New approach ● Deep learning pixel-to-pixel segmentation. ○ Hand labelled mask is needed. ○ Let’s generate it ! From : Ra Gyoung Yoon et al, “Quantitative assesment of change in regional disease patterns on serial HRCT of fibrotic interstitial pneumonia with texture-based automated quantification system”. 2012.
  16. 16. Mask generation ● A naive approach → Failed. ○ Because the neural network have learned deterministic patterns instead of lung disease patterns. Honeycombing Emphysema
  17. 17. Mask generation ● Ken Perlin, “An image Synthesizer”, 1985 ○ natural appearing textures ○ gradient based fractal noise ○ heavily used in game business
  18. 18. Mask generation ● One random Perlin noise ( simplex noise ) ● two randomly selected ROI patches ConsolidationGGO Mask ROI Patch
  19. 19. Mask generation ● 547 patches → Infinite patches ( O(1006xN ) )
  20. 20. Model architecture ● UNet + SWWAE architecture ○ Olaf et al, “U-Net: Convolutional Networks for Biomedical Image Segmentation”, 2015 ○ Junbo et al, “Stacked What-Where Auto-encoders”, 2015
  21. 21. Model architecture Skip connections
  22. 22. Deep learning approach ● pixel-to-pixel segmentation result
  23. 23. Deep learning approach ● pixel-to-pixel segmentation result
  24. 24. Deep learning approach ● pixel-to-pixel segmentation result
  25. 25. Deep learning approach ● pixel-to-pixel segmentation accuracy
  26. 26. High resolution segmentation ● 20 x 20 patches per 512 x 512 image ○ (512 - 20 + 1)2 → Too expensive
  27. 27. High resolution segmentation ● Fully convolutional layer used ○ Various sized image input available
  28. 28. High resolution segmentation ● 200 x 80 grids
  29. 29. High resolution segmentation ● 500 x 20 grid ( Vertical grids )
  30. 30. High resolution segmentation ● 20 x 500 grid ( Horizontal grids )
  31. 31. High resolution segmentation ● Computation complexity
  32. 32. High resolution segmentation ● Results ( Hortz )
  33. 33. High resolution segmentation ● Results ( Vert )
  34. 34. High resolution segmentation ● Results ( Mix )
  35. 35. High resolution segmentation ● Comparison - Accuracy
  36. 36. High resolution segmentation ● Comparison - computation time
  37. 37. Our contributions ● A simple and practical pixel mask generation method for DILD ROI dataset using Perlin noise. ○ No radiologist mask needed. ● We applied state-of-the-art deep CNN based pixel-to-pixel segmentation method to DILD dataset. ○ High accuracy with reasonable computing time.
  38. 38. Thank you !!!

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