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Accurate organ segmentation is crucial for prostate cancer radiotherapy, but conebeam computer tomography (CBCT) based models are hindered by lowimage quality and annotation scarcity.We propose a cross-modality supervision framework where a generative adversarial network translates planning CT (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. By eliminating the need for manual CBCT annotations, our method enables practical AI-driven segmentation for adaptive radiotherapy, achieving accuracy comparable to pCT-trained models [1].

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Abstract: 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 conebeam computer tomography (CBCT) based models are hindered by lowimage quality and annotation scarcity.We propose a cross-modality supervision framework where a generative adversarial network translates planning CT (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. By eliminating the need for manual CBCT annotations, our method enables practical AI-driven segmentation for adaptive radiotherapy, achieving accuracy comparable to pCT-trained models [1].