Segmentation of colorectal pathology images using level sets

Colorectal cancer is the third most common cancer diagnosed in men and women. Generally surgery is by total excision of the mesorectum (TME), though it often has a poor outcome due to affected lymph nodes close to the resection boundary. Advancements in diagnosis and treatment of colorectal cancer require integration of information from different sources such as pathology macroscopic and microscopic images and magnetic resonance images. Evaluation of the mesorectal fascia and the rectal wall are important for both staging the cancer and predicting the outcome of the TME. An algorithm is developed for segmentation of the rectal wall on macroscopic pathology slice images. The information is vital for registration of the images for reconstruction of the resected volume but more importantly for fusion of images in order to evaluate different measures and establish correspondences across modalities. The resected specimen is segmented from the background using thresholding. Following, a number of features such as intensity different order statistics and phase information are evaluated for the region of interest. The features are incorporated in a level set framework for the segmentation of the rectal wall.