Breast Lesions Segmentation using Dual-level UNet (DL-UNet)

Yanjiao Zhao, Zhihui Lai, Linlin Shen, Heng Kong

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Breast disease is one of the primary diseases endangering women's health. Accurate segmentation of breast lesions can help doctors diagnose breast diseases. However, the size and morphology of breast lesions are different, and the intensity of breast tissue is uneven. Thus, it is challenging to segment the lesion area accurately. In this paper, we propose Dual-scale Feature Fusion (DSFF) module and Edgeloss to segment breast lesions. The DSFF module aims to integrate two-scale features and design another effective skip connection scheme to reduce false positive regions. To solve the problem of unclear segmentation boundary, we design Edgeloss for additional supervision on the boundary region to obtain a finer segmentation boundary. The experiment results show that the proposed DL-UNet with the DSFF module and new Edgeloss performs best in several classic networks.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 35th International Symposium on Computer-Based Medical Systems, CBMS 2022
EditorsLinlin Shen, Alejandro Rodriguez Gonzalez, KC Santosh, Zhihui Lai, Rosa Sicilia, Joao Rafael Almeida, Bridget Kane
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages339-344
Number of pages6
ISBN (Electronic)9781665467704
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022 - Shenzhen, China
Duration: 21 Jul 202223 Jul 2022

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2022-July
ISSN (Print)1063-7125

Conference

Conference35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022
Country/TerritoryChina
CityShenzhen
Period21/07/2223/07/22

Keywords

  • boundary-based loss
  • breast lesions
  • Medical image segmentation
  • skip connection

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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