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Open source segmentation models often do not provide comprehensive segmentation of both organs-at-risk and target volumes. In order to bridge this gap, we utilize the NNUNet framework and create an end-to-end pipeline for prostate cancer segmentation. To demonstrate the real-world efficacy of our approach, we conduct a clinical case study. First, we prepare a new dataset from real prostate cancer patient data. Then, we discuss the diverse challenges encountered during this process and provide solutions to overcome those. Next, we evaluate the results based on clinical viability. The results not only showcase the accuracy and reliability of this pipeline, but also emphasize its potential for practical applications in Radiation Therapy Planning. Code is available at CHAVI-India/draw .

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End-to-End Prostate Cancer Segmentation for RT Planning

  • 会议论文

  • 发表日期：

    2026年

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  • Sandip Dutta,
  • Surajit Kundu,
  • Santam Chakraborty,
  • Indranil Mallick,
  • Sougata Maity,
  • Aranya Sarkar,
  • Soumyajit Das,
  • Sanjoy Chatterjee,
  • Rimpa Basu Achari,
  • Moses Arunsingh,
  • Tapesh Bhattacharyya,
  • Jayanta Mukhopadhyay,
  • Nishant Chakravorty

摘要

Open source segmentation models often do not provide comprehensive segmentation of both organs-at-risk and target volumes. In order to bridge this gap, we utilize the NNUNet framework and create an end-to-end pipeline for prostate cancer segmentation. To demonstrate the real-world efficacy of our approach, we conduct a clinical case study. First, we prepare a new dataset from real prostate cancer patient data. Then, we discuss the diverse challenges encountered during this process and provide solutions to overcome those. Next, we evaluate the results based on clinical viability. The results not only showcase the accuracy and reliability of this pipeline, but also emphasize its potential for practical applications in Radiation Therapy Planning. Code is available at CHAVI-India/draw .