Spatial interaction analysis with graph based mathematical morphology for histopathology

Bassem Ben Cheikh; Nicolas Elie; Benoit Plancoulaine; Catherine Bor-Angelier; Daniel Racoceanu

doi:10.1109/ISBI.2017.7950642

Spatial interaction analysis with graph based mathematical morphology for histopathology

Bassem Ben Cheikh
, Nicolas Elie
, Benoit Plancoulaine
, Catherine Bor-Angelier
, Daniel Racoceanu

Grupo de Formación y Procesamiento de Imágenes Médicas

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

4 Scopus citations

Abstract

Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification, using supervised learning algorithm, to detect lymphoid aggregates and tumor patterns, and spatial distribution quantification using sparse sets' mathematical morphology. Tumor patterns were classified into three groups: surrounded by lymphocytes, close to lymphoid aggregates or distant and might be protected from immune attack. The approach provides statistical assessment and comprehensive visual representation of the inflammatory tumor microenvironment.

Original language	English
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Publisher	IEEE Computer Society
Pages	813-817
Number of pages	5
ISBN (Electronic)	9781509011711
DOIs	https://doi.org/10.1109/ISBI.2017.7950642
State	Published - 15 Jun 2017
Event	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia Duration: 18 Apr 2017 → 21 Apr 2017

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	18/04/17 → 21/04/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Digital histopathology
Graph representation
Mathematical morphology
Spatial relation modeling
Tumor-immune system interaction

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10.1109/ISBI.2017.7950642

Cite this

Ben Cheikh, B., Elie, N., Plancoulaine, B., Bor-Angelier, C., & Racoceanu, D. (2017). Spatial interaction analysis with graph based mathematical morphology for histopathology. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 813-817). Article 7950642 (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950642

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title = "Spatial interaction analysis with graph based mathematical morphology for histopathology",

abstract = "Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification, using supervised learning algorithm, to detect lymphoid aggregates and tumor patterns, and spatial distribution quantification using sparse sets' mathematical morphology. Tumor patterns were classified into three groups: surrounded by lymphocytes, close to lymphoid aggregates or distant and might be protected from immune attack. The approach provides statistical assessment and comprehensive visual representation of the inflammatory tumor microenvironment.",

keywords = "Digital histopathology, Graph representation, Mathematical morphology, Spatial relation modeling, Tumor-immune system interaction",

author = "\{Ben Cheikh\}, Bassem and Nicolas Elie and Benoit Plancoulaine and Catherine Bor-Angelier and Daniel Racoceanu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 ; Conference date: 18-04-2017 Through 21-04-2017",

year = "2017",

month = jun,

day = "15",

doi = "10.1109/ISBI.2017.7950642",

language = "English",

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Ben Cheikh, B, Elie, N, Plancoulaine, B, Bor-Angelier, C & Racoceanu, D 2017, Spatial interaction analysis with graph based mathematical morphology for histopathology. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950642, Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, pp. 813-817, 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, 18/04/17. https://doi.org/10.1109/ISBI.2017.7950642

Spatial interaction analysis with graph based mathematical morphology for histopathology. / Ben Cheikh, Bassem; Elie, Nicolas; Plancoulaine, Benoit et al.
2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 813-817 7950642 (Proceedings - International Symposium on Biomedical Imaging).

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

TY - GEN

T1 - Spatial interaction analysis with graph based mathematical morphology for histopathology

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AU - Elie, Nicolas

AU - Plancoulaine, Benoit

AU - Bor-Angelier, Catherine

AU - Racoceanu, Daniel

PY - 2017/6/15

Y1 - 2017/6/15

N2 - Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification, using supervised learning algorithm, to detect lymphoid aggregates and tumor patterns, and spatial distribution quantification using sparse sets' mathematical morphology. Tumor patterns were classified into three groups: surrounded by lymphocytes, close to lymphoid aggregates or distant and might be protected from immune attack. The approach provides statistical assessment and comprehensive visual representation of the inflammatory tumor microenvironment.

AB - Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification, using supervised learning algorithm, to detect lymphoid aggregates and tumor patterns, and spatial distribution quantification using sparse sets' mathematical morphology. Tumor patterns were classified into three groups: surrounded by lymphocytes, close to lymphoid aggregates or distant and might be protected from immune attack. The approach provides statistical assessment and comprehensive visual representation of the inflammatory tumor microenvironment.

KW - Digital histopathology

KW - Graph representation

KW - Mathematical morphology

KW - Spatial relation modeling

KW - Tumor-immune system interaction

UR - https://www.scopus.com/pages/publications/85023194743

U2 - 10.1109/ISBI.2017.7950642

DO - 10.1109/ISBI.2017.7950642

M3 - Conference contribution

AN - SCOPUS:85023194743

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 813

EP - 817

BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017

PB - IEEE Computer Society

T2 - 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017

Y2 - 18 April 2017 through 21 April 2017

ER -

Ben Cheikh B, Elie N, Plancoulaine B, Bor-Angelier C, Racoceanu D. Spatial interaction analysis with graph based mathematical morphology for histopathology. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society. 2017. p. 813-817. 7950642. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2017.7950642

Spatial interaction analysis with graph based mathematical morphology for histopathology

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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