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

Xuanang Xu1, Chunfeng Lian2, Shuai Wang3

  • 1Department of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Medical Image Analysis
|July 4, 2021
PubMed
Summary

Accurate segmentation of the prostate bed (PB) in CT scans is crucial for post-prostatectomy radiotherapy. An asymmetric multi-task attention network (AMTA-Net) effectively segments the PB by leveraging surrounding organs-at-risk (OARs), improving accuracy and clinical workflow.

Keywords:
Attention mechanismComputed tomographyDeep learningMulti-taskProstate bedSegmentation

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Area of Science:

  • Medical imaging
  • Radiotherapy planning
  • Artificial intelligence in medicine

Background:

  • Accurate prostate bed (PB) segmentation is vital for post-prostatectomy radiotherapy to minimize side effects on organs-at-risk (OARs).
  • PB segmentation in CT images is challenging due to non-contrast boundaries and variable shapes, even for experienced physicians.

Purpose of the Study:

  • To develop an automated method for accurate PB segmentation in CT images.
  • To leverage the relationship between PB and OARs for improved segmentation accuracy.
  • To reduce the time and labor involved in clinical radiotherapy planning.

Main Methods:

  • Proposed an asymmetric multi-task attention network (AMTA-Net) for concurrent segmentation of PB and OARs.
  • Utilized a U-Net as a backbone for OAR segmentation, followed by an attention sub-network for PB segmentation.
  • Leveraged OAR features to guide and improve PB segmentation, mimicking expert delineation.

Main Results:

  • AMTA-Net significantly outperformed current clinical atlas-based segmentation methods.
  • AMTA-Net demonstrated superior performance compared to existing deep learning-based segmentation methods.
  • The proposed method showed particular strength in segmenting the most challenging and critical PB boundaries.

Conclusions:

  • AMTA-Net offers a valuable tool for accurate and efficient PB segmentation in clinical practice.
  • The method has the potential to streamline radiotherapy planning workflows and reduce physician workload.
  • The attention-based approach effectively addresses the challenges of segmenting non-contrast targets like the PB.