Memory Based Active Contour Algorithm using

Pixel-level Classified Images for Colon Crypt

Segmentation

Assaf Cohen, Ehud Rivlin, Ilan Shimshoni, Edmond Sabo

 

 

Abstract

 

 

In this paper we introduce a novel method for detection and segmentation of crypts in colon biopsies. Most of the approaches proposed in the literature try to segment the crypts using only the biopsy image without understanding the meaning of each pixel. The proposed method differs in that we segment the crypts using an automatically generated pixel-level classification image of the original biopsy image and handle the artifacts due to the sectioning process and variance in color, shape and size of the crypts. The biopsy image pixels are classified to nuclei, immune system, lumen, cytoplasm, stroma and goblet cells. The crypts are then segmented using a novel active contour approach, where the external force is determined by the semantics of each pixel and the model of the crypt. The active contour is applied for every lumen candidate detected using the pixel-level classification. Finally, a false positive crypt elimination process is performed to remove segmentation errors. This is done by measuring their adherence to the crypt model using the pixel level classification results. The method was tested on 54 biopsy images containing 4944 healthy and 2236 cancerous crypts, resulting in 87% detection of the crypts with 9% of false positive segments (segments that do not represent a crypt). The segmentation accuracy of the true positive segments is 96%.

 

 

 

We present all the results and ground truth (PLC and crypts) of all the images on which the algorithm was tested. For PLC we have manually classified 11 images while we manually segmented all the crypts in the dataset. Download. You are free to use this dataset but please cite:

 

A Cohen, E. Rivlin, I. Shimshoni, and E. Sabo

Memory Based Active Contour Algorithm using Pixel-level Classified Images for Colon Crypt Segmentation, accepted for publication in Computerized Medical Imaging and Graphics.