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ProstateZones – Segmentations of the prostatic zones and urethra for the PROSTATEx dataset

Holmlund, William ; Simkó, Attila ; Söderkvist, Karin ; Palásti, Péter ; Tótin, Szilvia ; Kalmár, Kamilla ; Domoki, Zsófia ; Fejes, Zsuzsanna ; Kincses, Zsigmond Tamás and Brynolfsson, Patrik LU orcid , et al. (2024) In Scientific Data 11(1).
Abstract

Manual segmentations are considered the gold standard for ground truth in machine learning applications. Such tasks are tedious and time-consuming, albeit necessary to train reliable models. In this work, we present a dataset with expert segmentations of the prostatic zones and urethra for 200 randomly selected patients from the PROSTATEx dataset. Notably, independent duplicate segmentations were performed for 40 patients, providing inter-reader variability data. This results in a total of 240 segmentations. This dataset can be used to train machine learning models or serve as an external test set for evaluating models trained on private data, thereby addressing a current gap in the field. The delineated structures and terminology... (More)

Manual segmentations are considered the gold standard for ground truth in machine learning applications. Such tasks are tedious and time-consuming, albeit necessary to train reliable models. In this work, we present a dataset with expert segmentations of the prostatic zones and urethra for 200 randomly selected patients from the PROSTATEx dataset. Notably, independent duplicate segmentations were performed for 40 patients, providing inter-reader variability data. This results in a total of 240 segmentations. This dataset can be used to train machine learning models or serve as an external test set for evaluating models trained on private data, thereby addressing a current gap in the field. The delineated structures and terminology adhere to the latest Prostate Imaging Reporting and Data Systems v2.1 guidelines, ensuring consistency.

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publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Data
volume
11
issue
1
article number
1097
publisher
Nature Publishing Group
external identifiers
  • scopus:85205955286
  • pmid:39379407
ISSN
2052-4463
DOI
10.1038/s41597-024-03945-2
language
English
LU publication?
no
additional info
Publisher Copyright: © The Author(s) 2024.
id
e757b2e7-4b73-41de-9131-c6927831280b
date added to LUP
2024-11-26 13:00:28
date last changed
2025-07-09 07:42:24
@article{e757b2e7-4b73-41de-9131-c6927831280b,
  abstract     = {{<p>Manual segmentations are considered the gold standard for ground truth in machine learning applications. Such tasks are tedious and time-consuming, albeit necessary to train reliable models. In this work, we present a dataset with expert segmentations of the prostatic zones and urethra for 200 randomly selected patients from the PROSTATEx dataset. Notably, independent duplicate segmentations were performed for 40 patients, providing inter-reader variability data. This results in a total of 240 segmentations. This dataset can be used to train machine learning models or serve as an external test set for evaluating models trained on private data, thereby addressing a current gap in the field. The delineated structures and terminology adhere to the latest Prostate Imaging Reporting and Data Systems v2.1 guidelines, ensuring consistency.</p>}},
  author       = {{Holmlund, William and Simkó, Attila and Söderkvist, Karin and Palásti, Péter and Tótin, Szilvia and Kalmár, Kamilla and Domoki, Zsófia and Fejes, Zsuzsanna and Kincses, Zsigmond Tamás and Brynolfsson, Patrik and Nyholm, Tufve}},
  issn         = {{2052-4463}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Data}},
  title        = {{ProstateZones – Segmentations of the prostatic zones and urethra for the PROSTATEx dataset}},
  url          = {{http://dx.doi.org/10.1038/s41597-024-03945-2}},
  doi          = {{10.1038/s41597-024-03945-2}},
  volume       = {{11}},
  year         = {{2024}},
}