Self-co-attention neural network for anatomy segmentation in whole breast ultrasound

Baiying Lei, Shan Huang, Hang Li, Ran Li, Cheng Bian, Yi Hong Chou, Jing Qin, Peng Zhou, Xuehao Gong, Jie Zhi Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)


The automated whole breast ultrasound (AWBUS) is a new breast imaging technique that can depict the whole breast anatomy. To facilitate the reading of AWBUS images and support the breast density estimation, an automatic breast anatomy segmentation method for AWBUS images is proposed in this study. The problem at hand is quite challenging as it needs to address issues of low image quality, ill-defined boundary, large anatomical variation, etc. To address these issues, a new deep learning encoder-decoder segmentation method based on a self-co-attention mechanism is developed. The self-attention mechanism is comprised of spatial and channel attention module (SC) and embedded in the ResNeXt (i.e., Res-SC) block in the encoder path. A non-local context block (NCB) is further incorporated to augment the learning of high-level contextual cues. The decoder path of the proposed method is equipped with the weighted up-sampling block (WUB) to attain class-specific better up-sampling effect. Meanwhile, the co-attention mechanism is also developed to improve the segmentation coherence among two consecutive slices. Extensive experiments are conducted with comparison to several the state-of-the-art deep learning segmentation methods. The experimental results corroborate the effectiveness of the proposed method on the difficult breast anatomy segmentation problem on AWBUS images.

Original languageEnglish
Article number101753
JournalMedical Image Analysis
Publication statusPublished - Aug 2020


  • Breast anatomy segmentation
  • Encoder-decoder architecture
  • Non-local cue
  • Self-co-attention mechanism

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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