LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset
Journal:
arXiv
Published Date:
Feb 6, 2025
Abstract
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.
