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　bound-aries　and　highly　variable　shapes　depending　on　neighboring　OARs.　In　this　work,　we　propose　an　asym-metric　multi-task　attention　network　(AMTA-Net)　for　the　concurrent　segmentation　of　PB　and　surround-ing　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　https://github.com/superxuang/amta-net.　Published　by　Elsevier　B.V.