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.
@article{arxiv.2502.04493,
title = {LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset},
author = {Viktor Rogowski and Lars E Olsson and Jonas Scherman and Emilia Persson and Mustafa Kadhim and Sacha af Wetterstedt and Adalsteinn Gunnlaugsson and Martin P. Nilsson and Nandor Vass and Mathieu Moreau and Maria Gebre Medhin and Sven Bäck and Per Munck af Rosenschöld and Silke Engelholm and Christian Jamtheim Gustafsson},
journal= {arXiv preprint arXiv:2502.04493},
year = {2025}
}