BGM-Net : Boundary-Guided Multiscale Network for Breast Lesion Segmentation in Ultrasound

Yunzhu WU, Ruoxin ZHANG, Lei ZHU, Weiming WANG, Shengwen WANG, Haoran XIE, Gary CHENG, Fu Lee WANG, Xingxiang HE*, Hai ZHANG*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Automatic and accurate segmentation of breast lesion regions from ultrasonography is an essential step for ultrasound-guided diagnosis and treatment. However, developing a desirable segmentation method is very difficult due to strong imaging artifacts e.g., speckle noise, low contrast and intensity inhomogeneity, in breast ultrasound images. To solve this problem, this paper proposes a novel boundary-guided multiscale network (BGM-Net) to boost the performance of breast lesion segmentation from ultrasound images based on the feature pyramid network (FPN). First, we develop a boundary-guided feature enhancement (BGFE) module to enhance the feature map for each FPN layer by learning a boundary map of breast lesion regions. The BGFE module improves the boundary detection capability of the FPN framework so that weak boundaries in ambiguous regions can be correctly identified. Second, we design a multiscale scheme to leverage the information from different image scales in order to tackle ultrasound artifacts. Specifically, we downsample each testing image into a coarse counterpart, and both the testing image and its coarse counterpart are input into BGM-Net to predict a fine and a coarse segmentation maps, respectively. The segmentation result is then produced by fusing the fine and the coarse segmentation maps so that breast lesion regions are accurately segmented from ultrasound images and false detections are effectively removed attributing to boundary feature enhancement and multiscale image information. We validate the performance of the proposed approach on two challenging breast ultrasound datasets, and experimental results demonstrate that our approach outperforms state-of-the-art methods.

Original languageEnglish
Article number698334
JournalFrontiers in Molecular Biosciences
Volume8
Early online date19 Jul 2021
DOIs
Publication statusPublished - Jul 2021

Bibliographical note

Funding Information:
This paper was supported by Natural Science Foundation of Shenzhen city (No. JCYJ20190806150001764), Natural Science Foundation of Guangdong province (No. 2020A1515010978), The Sanming Project of Medicine in Shenzhen training project (No. SYJY201802), National Natural Science Foundation of China (No. 61802072), General Research Fund (No. 18601118) of Research Grants Council of Hong Kong SAR, One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), Research Cluster Fund (RG 78/2019-2020R), Dean's Research Fund 2019/20 (FLASS/DRF/IDS-2) of The Education University of Hong Kong, and the Faculty Research Grant (DB21B6) of Lingnan University, Hong Kong.

Publisher Copyright:
© Copyright © 2021 Wu, Zhang, Zhu, Wang, Wang, Xie, Cheng, Wang, He and Zhang.

Keywords

  • boundary-guided feature enhancement
  • breast lesion segmentation
  • deep learning
  • multiscale image analysis
  • ultrasound image segmentation

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