LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset

TL;DR

This paper introduces a comprehensive, publicly available dataset of MRI, synthetic CT images, segmentations, and dose distributions for prostate cancer radiotherapy, supporting research in automated planning and deep learning.

Contribution

It provides a large, annotated dataset with expert-adjusted segmentations and uncertainty maps, facilitating advancements in prostate radiotherapy automation and model evaluation.

Findings

Dataset includes 432 patients with MRI and synthetic CT images.

Extended dataset with deep learning-generated and manually adjusted segmentations.

Resource supports research in automated treatment planning and model uncertainty.

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.