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Proceedings Paper

Hierarchical nucleus segmentation in digital pathology images
Author(s): Yi Gao; Vadim Ratner; Liangjia Zhu; Tammy Diprima; Tahsin Kurc; Allen Tannenbaum; Joel Saltz
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Paper Abstract

Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set.

Paper Details

Date Published: 23 March 2016
PDF: 6 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979117 (23 March 2016); doi: 10.1117/12.2217029
Show Author Affiliations
Yi Gao, Stony Brook Univ. (United States)
Vadim Ratner, Stony Brook Univ. (United States)
Liangjia Zhu, Stony Brook Univ. (United States)
Tammy Diprima, Stony Brook Univ. (United States)
Tahsin Kurc, Stony Brook Univ. (United States)
Allen Tannenbaum, Stony Brook Univ. (United States)
Joel Saltz, 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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