Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net

R. Rashmi; Keerthana Prasad; Chethana Babu K. Udupa

doi:10.1007/978-981-16-1086-8_13

Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net

R. Rashmi
, Keerthana Prasad^*
, Chethana Babu K. Udupa

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Breast cancer is a major disease in the world and is detected by histopathological image analysis. The structure and characteristics of nuclei contributes largely in the decision of malignancy of a tumor. There exists several medical image processing techniques based on traditional and CNN methods to segment nuclei from breast histopathological images. However, these algorithms use hand crafted features and depend on availability of large annotated dataset. Moreover, heterogeneous structure and characteristic of nuclei makes it non trivial task. In this context, this paper presents an encoder decoder based CNN architecture to semantically segment nuclei from breast histopathological images. A new attention mechanism is used to extract feature from the nuclei regions at multiple scales. The proposed architecture is evaluated on breast histopathological images and achieved an mIoU of 0.77.

Original language	English
Title of host publication	Computer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers
Editors	Satish Kumar Singh, Partha Roy, Balasubramanian Raman, P. Nagabhushan
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	137-148
Number of pages	12
ISBN (Print)	9789811610851
DOIs	https://doi.org/10.1007/978-981-16-1086-8_13
Publication status	Published - 2021
Event	5th International Conference on Computer Vision and Image Processing, CVIP 2020 - Prayagraj, India Duration: 04-12-2020 → 06-12-2020

Publication series

Name	Communications in Computer and Information Science
Volume	1376 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	5th International Conference on Computer Vision and Image Processing, CVIP 2020
Country/Territory	India
City	Prayagraj
Period	04-12-20 → 06-12-20

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

Access to Document

10.1007/978-981-16-1086-8_13

Cite this

Rashmi, R., Prasad, K., & Udupa, C. B. K. (2021). Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net. In S. K. Singh, P. Roy, B. Raman, & P. Nagabhushan (Eds.), Computer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers (pp. 137-148). (Communications in Computer and Information Science; Vol. 1376 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-1086-8_13

Rashmi, R. ; Prasad, Keerthana ; Udupa, Chethana Babu K. / Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net. Computer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers. editor / Satish Kumar Singh ; Partha Roy ; Balasubramanian Raman ; P. Nagabhushan. Springer Science and Business Media Deutschland GmbH, 2021. pp. 137-148 (Communications in Computer and Information Science).

@inproceedings{ecd4fa4c124e4f4f9a9be404194bb66a,

title = "Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net",

abstract = "Breast cancer is a major disease in the world and is detected by histopathological image analysis. The structure and characteristics of nuclei contributes largely in the decision of malignancy of a tumor. There exists several medical image processing techniques based on traditional and CNN methods to segment nuclei from breast histopathological images. However, these algorithms use hand crafted features and depend on availability of large annotated dataset. Moreover, heterogeneous structure and characteristic of nuclei makes it non trivial task. In this context, this paper presents an encoder decoder based CNN architecture to semantically segment nuclei from breast histopathological images. A new attention mechanism is used to extract feature from the nuclei regions at multiple scales. The proposed architecture is evaluated on breast histopathological images and achieved an mIoU of 0.77.",

author = "R. Rashmi and Keerthana Prasad and Udupa, \{Chethana Babu K.\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; 5th International Conference on Computer Vision and Image Processing, CVIP 2020 ; Conference date: 04-12-2020 Through 06-12-2020",

year = "2021",

doi = "10.1007/978-981-16-1086-8\_13",

language = "English",

isbn = "9789811610851",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "137--148",

editor = "Singh, \{Satish Kumar\} and Partha Roy and Balasubramanian Raman and P. Nagabhushan",

booktitle = "Computer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers",

address = "Germany",

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Rashmi, R , Prasad, K & Udupa, CBK 2021, Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net. in SK Singh, P Roy, B Raman & P Nagabhushan (eds), Computer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers. Communications in Computer and Information Science, vol. 1376 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 137-148, 5th International Conference on Computer Vision and Image Processing, CVIP 2020, Prayagraj, India, 04-12-20. https://doi.org/10.1007/978-981-16-1086-8_13

Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net. / Rashmi, R.; Prasad, Keerthana ; Udupa, Chethana Babu K.
Computer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers. ed. / Satish Kumar Singh; Partha Roy; Balasubramanian Raman; P. Nagabhushan. Springer Science and Business Media Deutschland GmbH, 2021. p. 137-148 (Communications in Computer and Information Science; Vol. 1376 CCIS).

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

TY - GEN

T1 - Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net

AU - Rashmi, R.

AU - Prasad, Keerthana

AU - Udupa, Chethana Babu K.

PY - 2021

Y1 - 2021

N2 - Breast cancer is a major disease in the world and is detected by histopathological image analysis. The structure and characteristics of nuclei contributes largely in the decision of malignancy of a tumor. There exists several medical image processing techniques based on traditional and CNN methods to segment nuclei from breast histopathological images. However, these algorithms use hand crafted features and depend on availability of large annotated dataset. Moreover, heterogeneous structure and characteristic of nuclei makes it non trivial task. In this context, this paper presents an encoder decoder based CNN architecture to semantically segment nuclei from breast histopathological images. A new attention mechanism is used to extract feature from the nuclei regions at multiple scales. The proposed architecture is evaluated on breast histopathological images and achieved an mIoU of 0.77.

AB - Breast cancer is a major disease in the world and is detected by histopathological image analysis. The structure and characteristics of nuclei contributes largely in the decision of malignancy of a tumor. There exists several medical image processing techniques based on traditional and CNN methods to segment nuclei from breast histopathological images. However, these algorithms use hand crafted features and depend on availability of large annotated dataset. Moreover, heterogeneous structure and characteristic of nuclei makes it non trivial task. In this context, this paper presents an encoder decoder based CNN architecture to semantically segment nuclei from breast histopathological images. A new attention mechanism is used to extract feature from the nuclei regions at multiple scales. The proposed architecture is evaluated on breast histopathological images and achieved an mIoU of 0.77.

UR - https://www.scopus.com/pages/publications/85107505434

UR - https://www.scopus.com/pages/publications/85107505434#tab=citedBy

U2 - 10.1007/978-981-16-1086-8_13

DO - 10.1007/978-981-16-1086-8_13

M3 - Conference contribution

AN - SCOPUS:85107505434

SN - 9789811610851

T3 - Communications in Computer and Information Science

SP - 137

EP - 148

BT - Computer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers

A2 - Singh, Satish Kumar

A2 - Roy, Partha

A2 - Raman, Balasubramanian

A2 - Nagabhushan, P.

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Conference on Computer Vision and Image Processing, CVIP 2020

Y2 - 4 December 2020 through 6 December 2020

ER -

Rashmi R , Prasad K , Udupa CBK. Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net. In Singh SK, Roy P, Raman B, Nagabhushan P, editors, Computer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 137-148. (Communications in Computer and Information Science). doi: 10.1007/978-981-16-1086-8_13

Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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