Multi-scale Learning Based Segmentation of Glands in Digital Colonrectal Pathology Images

Citation:

Yi Gao, William Liu, Shipra Arjun, Liangjia Zhu, Vadim Ratner, Tahsin Kurc, Joel Saltz, and Allen Tannenbaum. 2016. “Multi-scale Learning Based Segmentation of Glands in Digital Colonrectal Pathology Images.” Proc SPIE Int Soc Opt Eng, 9791.

Abstract:

Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation. After the learning step, the dictionaries are used collectively to perform the classification and segmentation for the new image.

Last updated on 03/27/2017