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A pixel to-pixel segmentation method of DILD without masks using CNN and perlin noise
- 1. A pixel-to-pixel segmentation of DILD without masks using CNN and Perlin noise 2016.11 njkim@jamonglab.com
- 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. Challenges ● Small dataset ○ only 547 ROI ( 20x20 bounding box ) patches ● No human mask label ○ Extremely expensive
- 4. Dataset
- 5. Dataset
- 6. Dataset
- 7. Dataset
- 8. Traditional approach ● Superpixel approach
- 9. Traditional approach ● Superpixel result - factor 0.25
- 10. Traditional approach ● Superpixel result - factor 2
- 11. Traditional approach ● Superpixel result - factor 4
- 12. Traditional approach ● Superpixel result - factor 9
- 13. Traditional approach ● Superpixel accuracy
- 14. Traditional approach ● Superpixel limitation ○ deterministic and strong assumption ( Similarity of neighboring pixels )
- 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. Mask generation ● A naive approach → Failed. ○ Because the neural network have learned deterministic patterns instead of lung disease patterns. Honeycombing Emphysema
- 17. Mask generation ● Ken Perlin, “An image Synthesizer”, 1985 ○ natural appearing textures ○ gradient based fractal noise ○ heavily used in game business
- 18. Mask generation ● One random Perlin noise ( simplex noise ) ● two randomly selected ROI patches ConsolidationGGO Mask ROI Patch
- 19. Mask generation ● 547 patches → Infinite patches ( O(1006xN ) )
- 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
- 22. Deep learning approach ● pixel-to-pixel segmentation result
- 23. Deep learning approach ● pixel-to-pixel segmentation result
- 24. Deep learning approach ● pixel-to-pixel segmentation result
- 25. Deep learning approach ● pixel-to-pixel segmentation accuracy
- 26. High resolution segmentation ● 20 x 20 patches per 512 x 512 image ○ (512 - 20 + 1)2 → Too expensive
- 27. High resolution segmentation ● Fully convolutional layer used ○ Various sized image input available
- 28. High resolution segmentation ● 200 x 80 grids
- 29. High resolution segmentation ● 500 x 20 grid ( Vertical grids )
- 30. High resolution segmentation ● 20 x 500 grid ( Horizontal grids )
- 31. High resolution segmentation ● Computation complexity
- 32. High resolution segmentation ● Results ( Hortz )
- 33. High resolution segmentation ● Results ( Vert )
- 34. High resolution segmentation ● Results ( Mix )
- 35. High resolution segmentation ● Comparison - Accuracy
- 36. High resolution segmentation ● Comparison - computation time
- 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. Thank you !!!