公开432例前列腺放疗患者影像与分割数据，助力自动化治疗规划研究。
LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset
- 构建包含MRI、sCT及剂量分布的前列腺放疗多模态数据集
- 涵盖432例真实临床患者，含4位医生手动修正的深度学习分割结果
- 适合医学图像分割、放疗自动化及模型不确定性研究者使用
前列腺癌放疗依赖CT和/或MRI进行靶区和危机器官（OARs）分割。人工分割被视为机器学习的金标准，但耗时费力。本文发布一个公开临床数据集，包含432例接受MRI引导放疗的前列腺癌患者的数据，包括MRI和合成CT（sCT）图像、靶区与OARs分割结果、以及放疗剂量分布。另附扩展数据集，涵盖35例患者，新增深度学习（DL）生成的分割结果、分割不确定性图、以及四位放射肿瘤科医生手动修正后的结果。该资源旨在支持自动化放疗计划制定、分割、观察者间差异分析及深度学习模型不确定性研究。数据集托管于AIDA Data Hub，可免费获取，为医学影像与前列腺癌放疗研究提供重要支持。
原文摘要 · Abstract (English)
Radiotherapy treatment for prostate cancer relies on computed tomography (CT) and/or magnetic resonance imaging (MRI) for segmentation of target volumes and organs at risk (OARs). Manual segmentation of these volumes is regarded as the gold standard for ground truth in machine learning applications but to acquire such data is tedious and time-consuming. A publicly available clinical dataset is presented, comprising MRI- and synthetic CT (sCT) images, target and OARs segmentations, and radiotherapy dose distributions for 432 prostate cancer patients treated with MRI-guided radiotherapy. An extended dataset with 35 patients is also included, with the addition of deep learning (DL)-generated segmentations, DL segmentation uncertainty maps, and DL segmentations manually adjusted by four radiation oncologists. The publication of these resources aims to aid research within the fields of automated radiotherapy treatment planning, segmentation, inter-observer analyses, and DL model uncertainty investigation. The dataset is hosted on the AIDA Data Hub and offers a free-to-use resource for the scientific community, valuable for the advancement of medical imaging and prostate cancer radiotherapy research.
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