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Viktor Rogowski^1,2, Lars E Olsson^1,3, Jonas Scherman¹

¹Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.

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|April 11, 2025

Summary

A new dataset aids prostate cancer radiotherapy research by providing MRI/CT images and segmentations for 432 patients. It supports automated planning, segmentation, and deep learning model uncertainty analysis.

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Area of Science:

Medical Imaging
Radiation Oncology
Machine Learning

Background:

Radiotherapy for prostate cancer requires accurate segmentation of target volumes and organs at risk (OARs) using CT and/or MRI.
Manual segmentation, the gold standard, is time-consuming and labor-intensive, hindering machine learning applications.

Purpose of the Study:

To present a comprehensive, publicly available clinical dataset for prostate cancer radiotherapy research.
To facilitate advancements in automated treatment planning, segmentation accuracy, and deep learning model uncertainty assessment.

Main Methods:

A dataset of MRI and synthetic CT (sCT) images, segmentations, and dose distributions for 432 prostate cancer patients was compiled.
An extended dataset included deep learning (DL) segmentations, uncertainty maps, and expert-adjusted segmentations for 35 patients.

Main Results:

The dataset provides a valuable resource for training and validating machine learning models in radiotherapy.
It enables research into automated segmentation, inter-observer variability, and DL model uncertainty quantification.

Conclusions:

This publicly accessible dataset accelerates research in medical imaging and prostate cancer radiotherapy.
It supports the development of more efficient and accurate automated radiotherapy treatment planning systems.