a pixel to-pixel segmentation method of dild without masks using cnn and perlin noise
TRANSCRIPT
A pixel-to-pixel segmentation of DILD without masks
using CNN and Perlin noise
2016.11 [email protected]
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.
Challenges
● Small dataset○ only 547 ROI ( 20x20 bounding box ) patches
● No human mask label○ Extremely expensive
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 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.
Mask generation● A naive approach → Failed.
○ Because the neural network have learned deterministic patterns instead of lung disease patterns.
Honeycombing
Emphysema
Mask generation● Ken Perlin, “An image Synthesizer”, 1985
○ natural appearing textures○ gradient based fractal noise○ heavily used in game business
Mask generation● One random Perlin noise ( simplex noise )● two randomly selected ROI patches
ConsolidationGGO
Mask ROI Patch
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
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.