23 March 2016 Multi-scale learning based segmentation of glands in digital colonrectal pathology images
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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.
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Yi Gao, Yi Gao, William Liu, William Liu, Shipra Arjun, Shipra Arjun, Liangjia Zhu, Liangjia Zhu, Vadim Ratner, Vadim Ratner, Tahsin Kurc, Tahsin Kurc, Joel Saltz, Joel Saltz, Allen Tannenbaum, 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); doi: 10.1117/12.2216790; https://doi.org/10.1117/12.2216790
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