Multi-scale learning based segmentation of glands in digital colonrectal pathology images

Yi Gao; William Liu; Shipra Arjun; Liangjia Zhu; Vadim Ratner; Tahsin Kurc; Joel Saltz M.D.; Allen Tannenbaum

doi:10.1117/12.2216790

23 March 2016 Multi-scale learning based segmentation of glands in digital colonrectal pathology images

Yi Gao, William Liu, Shipra Arjun, Liangjia Zhu, Vadim Ratner, Tahsin Kurc, Joel Saltz M.D., Allen Tannenbaum

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 97910M (2016) https://doi.org/10.1117/12.2216790
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Yi Gao, William Liu, Shipra Arjun, Liangjia Zhu, Vadim Ratner, Tahsin Kurc, Joel Saltz M.D., and Allen Tannenbaum "Multi-scale learning based segmentation of glands in digital colonrectal pathology images", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910M (23 March 2016); https://doi.org/10.1117/12.2216790

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