Invasive Region Segmentation using Pre-trained UNet and Prognosis Analysis of Breast Cancer based on Tumor-Stroma Ratio

Subrata Bhattacharjee, Yeong Byn Hwang, Hee Cheol Kim, Heung Kook Choi, Dongmin Kim, Nam Hoon Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Breast cancer (BCa) is a type of disease that has multiple prognostic markers that differs from one cancer stage to another. Assessing the area and pattern of cancer regions is essential for pathological investigations. However, the main purpose of this study is to segment the regions of invasive carcinoma (i.e., non-tubular and tubular) in the histological sections of BCa. The segmentation was performed on hematoxylin and eosin (H&E)-stained tissue slides of 42 BCa patients from 5 different centers in Korea. The tumor-stroma ratio (TSR) is a promising prognostic parameter for BCa as well as in other epithelial cancer types estimating the area of stroma and tumor regions. Therefore, in this paper, we used the pre-trained convolutional neural network (CNN) models as a backbone of UNet, to precisely extract the tumor regions from the stroma tissue components for TSR analysis.

Original languageEnglish
Title of host publicationInternational Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665470872
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 - Prague, Czech Republic
Duration: 2022 Jul 202022 Jul 22

Publication series

NameInternational Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

Conference

Conference2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
Country/TerritoryCzech Republic
CityPrague
Period22/7/2022/7/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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