Hierarchical nucleus segmentation in digital pathology images.

Autor:	Gao Y; Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A; Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A., Ratner V; Department of Computer Science, Stony Brook University, NY, U.S.A., Zhu L; Department of Computer Science, Stony Brook University, NY, U.S.A., Diprima T; Department of Biomedical Informatics, Stony Brook University, NY, U.S.A., Kurc T; Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A., Tannenbaum A; Department of Computer Science, Stony Brook University, NY, U.S.A; Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A., Saltz J; Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A.
Jazyk:	angličtina
Zdroj:	Proceedings of SPIE--the International Society for Optical Engineering [Proc SPIE Int Soc Opt Eng] 2016 Feb; Vol. 9791. Date of Electronic Publication: 2016 Mar 23.
DOI:	10.1117/12.2217029
Abstrakt:	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.
Databáze:	MEDLINE
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