Batch-invariant nuclear segmentation in whole mount histology sections

Hang Chang; Leandro A. Loss; Paul T. Spellman; Alexander D Borowsky; Bahram Parvin

doi:10.1109/ISBI.2012.6235683

Batch-invariant nuclear segmentation in whole mount histology sections

Hang Chang, Leandro A. Loss, Paul T. Spellman, Alexander D Borowsky, Bahram Parvin

Pathology and Laboratory Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

The Cancer Genome Atlas (TCGA) provides a rich repository of whole mount tumor sections that are collected from different laboratories. However, there are a significant amount of technical and biological variations that impede analysis. We have developed a novel approach for nuclear segmentation in histology sections, which addresses the problem of technical and biological variations by incorporating information from manually annotated reference patches with the local color space of the original image. Subsequently, the problem is formulated within a multi-reference graph cut with geodesic constraints. This approach has been validated on manually curated samples and then applied to a dataset of 440 whole mount tissue sections, originating from different laboratories, which are typically 40k-by-40k pixels or larger. Segmentation results, through a zoomable interface, and extracted morphometric data are available at: http://tcga.lbl.gov.

Original language	English (US)
Title of host publication	Proceedings - International Symposium on Biomedical Imaging
Pages	856-859
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2012.6235683
State	Published - 2012
Event	2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain Duration: May 2 2012 → May 5 2012

Other

Other	2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country	Spain
City	Barcelona
Period	5/2/12 → 5/5/12

Keywords

H&E tissue section
Nuclear segmentation
Nuclear/Background classification

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

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Chang, H, Loss, LA, Spellman, PT, Borowsky, AD & Parvin, B 2012, Batch-invariant nuclear segmentation in whole mount histology sections. in Proceedings - International Symposium on Biomedical Imaging., 6235683, pp. 856-859, 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012, Barcelona, Spain, 5/2/12. https://doi.org/10.1109/ISBI.2012.6235683

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abstract = "The Cancer Genome Atlas (TCGA) provides a rich repository of whole mount tumor sections that are collected from different laboratories. However, there are a significant amount of technical and biological variations that impede analysis. We have developed a novel approach for nuclear segmentation in histology sections, which addresses the problem of technical and biological variations by incorporating information from manually annotated reference patches with the local color space of the original image. Subsequently, the problem is formulated within a multi-reference graph cut with geodesic constraints. This approach has been validated on manually curated samples and then applied to a dataset of 440 whole mount tissue sections, originating from different laboratories, which are typically 40k-by-40k pixels or larger. Segmentation results, through a zoomable interface, and extracted morphometric data are available at: http://tcga.lbl.gov.",

keywords = "H&E tissue section, Nuclear segmentation, Nuclear/Background classification",

author = "Hang Chang and Loss, {Leandro A.} and Spellman, {Paul T.} and Borowsky, {Alexander D} and Bahram Parvin",

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AU - Loss, Leandro A.

AU - Spellman, Paul T.

AU - Borowsky, Alexander D

AU - Parvin, Bahram

PY - 2012

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AB - The Cancer Genome Atlas (TCGA) provides a rich repository of whole mount tumor sections that are collected from different laboratories. However, there are a significant amount of technical and biological variations that impede analysis. We have developed a novel approach for nuclear segmentation in histology sections, which addresses the problem of technical and biological variations by incorporating information from manually annotated reference patches with the local color space of the original image. Subsequently, the problem is formulated within a multi-reference graph cut with geodesic constraints. This approach has been validated on manually curated samples and then applied to a dataset of 440 whole mount tissue sections, originating from different laboratories, which are typically 40k-by-40k pixels or larger. Segmentation results, through a zoomable interface, and extracted morphometric data are available at: http://tcga.lbl.gov.

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KW - Nuclear segmentation

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