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

Rogowski, Viktor LU ; Olsson, Lars E. LU orcid ; Scherman, Jonas ; Persson, Emilia LU ; Kadhim, Mustafa LU ; af Wetterstedt, Sacha LU ; Gunnlaugsson, Adalsteinn LU ; Nilsson, Martin P. LU ; Vass, Nandor and Moreau, Mathieu , et al. (2025) In Scientific Data 12. p.1-9
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... (More)

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 in 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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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Data
volume
12
article number
611
pages
1 - 9
publisher
Nature Publishing Group
external identifiers
  • scopus:105003323848
  • pmid:40216786
ISSN
2052-4463
DOI
10.1038/s41597-025-04954-5
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s) 2025.
id
021d1b70-894d-4981-9c5a-5627ef929468
date added to LUP
2025-09-01 22:12:53
date last changed
2025-09-30 00:51:18
@article{021d1b70-894d-4981-9c5a-5627ef929468,
  abstract     = {{<p>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 in 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.</p>}},
  author       = {{Rogowski, Viktor and Olsson, Lars E. and Scherman, Jonas and Persson, Emilia and Kadhim, Mustafa and af Wetterstedt, Sacha and Gunnlaugsson, Adalsteinn and Nilsson, Martin P. and Vass, Nandor and Moreau, Mathieu and Gebre Medhin, Maria and Bäck, Sven and Munck af Rosenschöld, Per and Engelholm, Silke and Jamtheim Gustafsson, Christian}},
  issn         = {{2052-4463}},
  language     = {{eng}},
  pages        = {{1--9}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Data}},
  title        = {{LUND-PROBE – LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset}},
  url          = {{http://dx.doi.org/10.1038/s41597-025-04954-5}},
  doi          = {{10.1038/s41597-025-04954-5}},
  volume       = {{12}},
  year         = {{2025}},
}