Asymmetric multi-task attention network for prostate bed segmentation in computed tomography images

Xuanang Xu, Chunfeng Lian, Shuai Wang, Tong Zhu, Ronald C. Chen, Andrew Z. Wang, Trevor J. Royce, Pew Thian Yap, Dinggang Shen, Jun Lian

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Post-prostatectomy radiotherapy requires accurate annotation of the prostate bed (PB), i.e., the residual tissue after the operative removal of the prostate gland, to minimize side effects on surrounding organs-at-risk (OARs). However, PB segmentation in computed tomography (CT) images is a challenging task, even for experienced physicians. This is because PB is almost a "virtual" target with non-contrast boundaries and highly variable shapes depending on neighboring OARs. In this work, we propose an asymmetric multi-task attention network (AMTA-Net) for the concurrent segmentation of PB and surrounding OARs. Our AMTA-Net mimics experts in delineating the non-contrast PB by explicitly leveraging its critical dependency on the neighboring OARs (i.e., the bladder and rectum), which are relatively easy to distinguish in CT images. Specifically, we first adopt a U-Net as the backbone network for the low-level (or prerequisite) task of the OAR segmentation. Then, we build an attention sub-network upon the backbone U-Net with a series of cascaded attention modules, which can hierarchically transfer the OAR features and adaptively learn discriminative representations for the high-level (or primary) task of the PB segmentation. We comprehensively evaluate the proposed AMTA-Net on a clinical dataset composed of 186 CT images. According to the experimental results, our AMTA-Net significantly outperforms current clinical state-of-the-arts (i.e., atlas-based segmentation methods), indicating the value of our method in reducing time and labor in the clinical workflow. Our AMTA-Net also presents better performance than the technical state-of-the-arts (i.e., the deep learning-based segmentation methods), especially for the most indistinguishable and clinically critical part of the PB boundaries. Source code is released at

Original languageEnglish
Pages (from-to)102116
Number of pages1
JournalMedical Image Analysis
StatePublished - Aug 1 2021


  • Attention mechanism
  • Computed tomography
  • Deep learning
  • Multi-task
  • Prostate bed
  • Segmentation


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