LUND-PROBE – LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset
(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.
(Less)
- author
- organization
- publishing date
- 2025
- 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
-
- pmid:40216786
- scopus:105003323848
- 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
- 2025-09-03 03:26:44
@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}}, }