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Accurate organ segmentation is crucial for prostate cancer radiotherapy, but cone-beam computer tomography (CBCT) based models are hindered by low image quality and annotation scarcity. Existing approaches rely on deformable registration, which struggles with soft-tissue deformations, or direct CBCT training, which suffers from domain shifts and low-quality labels. We propose a domain adaptation framework that enables robust prostate segmentation on CBCT using cross-modality supervision from planning CT (pCT). A cycle-consistent generative adversarial network translates pCT into synthetic CBCT, enabling segmentation models to train on high-quality pCT-derived annotations while adapting to CBCT characteristics. Additionally, anatomy-aware augmentation enhances robustness to organ deformations across diverse patient anatomies. Using a multi-center dataset, our approach achieves segmentation accuracy comparable to pCT-trained models. By eliminating the need for manual CBCT annotations, our method enables practical AI-driven segmentation for adaptive radiotherapy.

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Cross-Modality Supervised Prostate Segmentation on CBCT for Adaptive Radiotherapy

  • 会议论文

  • 发表日期：

    2026年

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  • Balint Kovacs,
  • Goran Stanic,
  • Fabian Weykamp,
  • Florian Ebert,
  • Dimitrios Bounias,
  • Bouchra Tawk,
  • Martin Niklas,
  • Jakob Liermann,
  • Oliver Jäkel,
  • Klaus H. Maier-Hein,
  • Ralf Floca,
  • Kristina Giske

摘要

Accurate organ segmentation is crucial for prostate cancer radiotherapy, but cone-beam computer tomography (CBCT) based models are hindered by low image quality and annotation scarcity. Existing approaches rely on deformable registration, which struggles with soft-tissue deformations, or direct CBCT training, which suffers from domain shifts and low-quality labels. We propose a domain adaptation framework that enables robust prostate segmentation on CBCT using cross-modality supervision from planning CT (pCT). A cycle-consistent generative adversarial network translates pCT into synthetic CBCT, enabling segmentation models to train on high-quality pCT-derived annotations while adapting to CBCT characteristics. Additionally, anatomy-aware augmentation enhances robustness to organ deformations across diverse patient anatomies. Using a multi-center dataset, our approach achieves segmentation accuracy comparable to pCT-trained models. By eliminating the need for manual CBCT annotations, our method enables practical AI-driven segmentation for adaptive radiotherapy.