@inproceedings{a6996d1a285c4d2da3e09e61847e566d,
title = "Hierarchical nucleus segmentation in digital pathology images",
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.",
keywords = "digital pathology, nucleus segmentation",
author = "Yi Gao and Vadim Ratner and Liangjia Zhu and Tammy Diprima and Tahsin Kurc and Allen Tannenbaum and Joel Saltz",
note = "Publisher Copyright: {\textcopyright} 2016 SPIE.; 4th Medical Imaging 2016: Digital Pathology ; Conference date: 02-03-2016 Through 03-03-2016",
year = "2016",
doi = "10.1117/12.2217029",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Anant Madabhushi and Gurcan, \{Metin N.\}",
booktitle = "Medical Imaging 2016",
}