Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue

Kengo Araki; Mariyo Rokutan-Kurata; Kazuhiro Terada; Akihiko Yoshizawa; Ryoma Bise

doi:10.1109/EMBC46164.2021.9630809

Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue

Kengo Araki, Mariyo Rokutan-Kurata, Kazuhiro Terada, Akihiko Yoshizawa, Ryoma Bise

Practical Data Science

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

Abstract

Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance compared with the conventional method.

Original language	English
Title of host publication	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3328-3331
Number of pages	4
ISBN (Electronic)	9781728111797
DOIs	https://doi.org/10.1109/EMBC46164.2021.9630809
Publication status	Published - 2021
Event	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico Duration: Nov 1 2021 → Nov 5 2021

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Country/Territory	Mexico
City	Virtual, Online
Period	11/1/21 → 11/5/21

All Science Journal Classification (ASJC) codes

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

UN SDGs

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

Access to Document

10.1109/EMBC46164.2021.9630809

Cite this

Araki, K., Rokutan-Kurata, M., Terada, K., Yoshizawa, A., & Bise, R. (2021). Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 (pp. 3328-3331). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC46164.2021.9630809

Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue. / Araki, Kengo; Rokutan-Kurata, Mariyo; Terada, Kazuhiro et al.

43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 3328-3331 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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

Araki, K, Rokutan-Kurata, M, Terada, K, Yoshizawa, A & Bise, R 2021, Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue. in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 3328-3331, 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Virtual, Online, Mexico, 11/1/21. https://doi.org/10.1109/EMBC46164.2021.9630809

Araki K, Rokutan-Kurata M, Terada K, Yoshizawa A, Bise R. Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 3328-3331. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC46164.2021.9630809

Araki, Kengo ; Rokutan-Kurata, Mariyo ; Terada, Kazuhiro et al. / Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 3328-3331 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue",

abstract = "Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance compared with the conventional method.",

author = "Kengo Araki and Mariyo Rokutan-Kurata and Kazuhiro Terada and Akihiko Yoshizawa and Ryoma Bise",

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AB - Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance compared with the conventional method.

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Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue

Abstract

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UN SDGs

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