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Gao Y, Ratner V, Zhu L, et al. Hierarchical nucleus segmentation in digital pathology images. Proc SPIE Int Soc Opt Eng. 2016;9791doi: 10.1117/12.2217029.
Gao, Y., Ratner, V., Zhu, L., Diprima, T., Kurc, T., Tannenbaum, A., & Saltz, J. (2016). Hierarchical nucleus segmentation in digital pathology images. Proceedings of SPIE--the International Society for Optical Engineering, 9791. https://doi.org/10.1117/12.2217029
Gao, Yi, et al. "Hierarchical nucleus segmentation in digital pathology images." Proceedings of SPIE--the International Society for Optical Engineering vol. 9791 (2016). doi: https://doi.org/10.1117/12.2217029
Gao Y, Ratner V, Zhu L, Diprima T, Kurc T, Tannenbaum A, Saltz J. Hierarchical nucleus segmentation in digital pathology images. Proc SPIE Int Soc Opt Eng. 2016 Feb;9791. doi: 10.1117/12.2217029. Epub 2016 Mar 23. PMID: 27375315; PMCID: PMC4927003.
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Proc SPIE Int Soc Opt Eng. 2016 Feb;9791. doi: 10.1117/12.2217029. Epub 2016 Mar 23.

Hierarchical nucleus segmentation in digital pathology images.

Proceedings of SPIE--the International Society for Optical Engineering

Yi Gao, Vadim Ratner, Liangjia Zhu, Tammy Diprima, Tahsin Kurc, Allen Tannenbaum, Joel Saltz

Affiliations

  1. 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.
  2. Department of Computer Science, Stony Brook University, NY, U.S.A.
  3. Department of Biomedical Informatics, Stony Brook University, NY, U.S.A.
  4. Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A.
  5. Department of Computer Science, Stony Brook University, NY, U.S.A; Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A.

PMID: 27375315 PMCID: PMC4927003 DOI: 10.1117/12.2217029

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

References

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Publication Types

Grant support