TY - JOUR
T1 - BGM-Net
T2 - Boundary-Guided Multiscale Network for Breast Lesion Segmentation in Ultrasound
AU - Wu, Yunzhu
AU - Zhang, Ruoxin
AU - Zhu, Lei
AU - Wang, Weiming
AU - Wang, Shengwen
AU - Xie, Haoran
AU - Cheng, Gary
AU - Wang, Fu Lee
AU - He, Xingxiang
AU - Zhang, Hai
N1 - Publisher Copyright:
© Copyright © 2021 Wu, Zhang, Zhu, Wang, Wang, Xie, Cheng, Wang, He and Zhang.
PY - 2021/7/19
Y1 - 2021/7/19
N2 - 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.
AB - 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.
KW - boundary-guided feature enhancement
KW - breast lesion segmentation
KW - deep learning
KW - multiscale image analysis
KW - ultrasound image segmentation
UR - http://www.scopus.com/inward/record.url?scp=85111921224&partnerID=8YFLogxK
U2 - 10.3389/fmolb.2021.698334
DO - 10.3389/fmolb.2021.698334
M3 - Article
AN - SCOPUS:85111921224
VL - 8
JO - Frontiers in Molecular Biosciences
JF - Frontiers in Molecular Biosciences
M1 - 698334
ER -