Cross-Model Attention-Guided Tumor Segmentation for 3D Automated Breast Ultrasound (ABUS) Images

Yue Zhou, Houjin Chen, Yanfeng Li, Xuyang Cao, Shu Wang, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Tumor segmentation in 3D automated breast ultrasound (ABUS) plays an important role in breast disease diagnosis and surgical planning. However, automatic segmentation of tumors in 3D ABUS images is still challenging, due to the large tumor shape and size variations, and uncertain tumor locations among patients. In this paper, we develop a novel cross-model attention-guided tumor segmentation network with a hybrid loss for 3D ABUS images. Specifically, we incorporate the tumor location into a segmentation network by combining an improved 3D Mask R-CNN head into V-Net as an end-to-end architecture. Furthermore, we introduce a cross-model attention mechanism that is able to aggregate the segmentation probability map from the improved 3D Mask R-CNN to each feature extraction level in the V-Net. Then, we design a hybrid loss to balance the contribution of each part in the proposed cross-model segmentation network. We conduct extensive experiments on 170 3D ABUS from 107 patients. Experimental results show that our method outperforms other state-of-the-art methods, by achieving the Dice similarity coefficient (DSC) of 64.57%, Jaccard coefficient (JC) of 53.39%, recall (REC) of 64.43%, precision (PRE) of 74.51%, 95th Hausdorff distance (95HD) of 11.91mm, and average surface distance (ASD) of 4.63mm. Our code will be available online (

Original languageEnglish
JournalIEEE Journal of Biomedical and Health Informatics
Publication statusAccepted/In press - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:


  • Attention mechanism
  • Automated breast ultrasound (ABUS)
  • Breast
  • Breast cancer
  • Cross-model segmentation
  • Feature extraction
  • Hybrid loss
  • Image segmentation
  • Three-dimensional displays
  • Tumors
  • Ultrasonic imaging

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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