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Proceedings Paper

Multi-scale learning based segmentation of glands in digital colonrectal pathology images
Author(s): Yi Gao; William Liu; Shipra Arjun; Liangjia Zhu; Vadim Ratner; Tahsin Kurc; Joel Saltz; Allen Tannenbaum
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Paper 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.

Paper Details

Date Published: 23 March 2016
PDF: 6 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910M (23 March 2016); doi: 10.1117/12.2216790
Show Author Affiliations
Yi Gao, Stony Brook Univ. (United States)
William Liu, Buckingham Browne & Nichols School (United States)
Shipra Arjun, Stony Brook Univ. (United States)
Liangjia Zhu, Stony Brook Univ. (United States)
Vadim Ratner, Stony Brook Univ. (United States)
Tahsin Kurc, Stony Brook Univ. (United States)
Joel Saltz, Stony Brook Univ. (United States)
Allen Tannenbaum, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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