Prostate158 - An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection.

Algorithms Humans Magnetic Resonance Imaging Male Prostate Prostatic Neoplasms Reproducibility of Results Retrospective Studies
Artificial intelligence Biparametric prostate MRI Deep learning Machine learning Magnetic resonance imaging Prostate cancer

Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
09 2022
Historique:
received: 03 04 2022
revised: 12 06 2022
accepted: 03 07 2022
pubmed: 17 7 2022
medline: 31 8 2022
entrez: 16 7 2022
Statut: ppublish

Résumé

The development of deep learning (DL) models for prostate segmentation on magnetic resonance imaging (MRI) depends on expert-annotated data and reliable baselines, which are often not publicly available. This limits both reproducibility and comparability. Prostate158 consists of 158 expert annotated biparametric 3T prostate MRIs comprising T2w sequences and diffusion-weighted sequences with apparent diffusion coefficient maps. Two U-ResNets trained for segmentation of anatomy (central gland, peripheral zone) and suspicious lesions for prostate cancer (PCa) with a PI-RADS score of ≥4 served as baseline algorithms. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average surface distance (ASD). The Wilcoxon test with Bonferroni correction was used to evaluate differences in performance. The generalizability of the baseline model was assessed using the open datasets Medical Segmentation Decathlon and PROSTATEx. Compared to Reader 1, the models achieved a DSC/HD/ASD of 0.88/18.3/2.2 for the central gland, 0.75/22.8/1.9 for the peripheral zone, and 0.45/36.7/17.4 for PCa. Compared with Reader 2, the DSC/HD/ASD were 0.88/17.5/2.6 for the central gland, 0.73/33.2/1.9 for the peripheral zone, and 0.4/39.5/19.1 for PCa. Interrater agreement measured in DSC/HD/ASD was 0.87/11.1/1.0 for the central gland, 0.75/15.8/0.74 for the peripheral zone, and 0.6/18.8/5.5 for PCa. Segmentation performances on the Medical Segmentation Decathlon and PROSTATEx were 0.82/22.5/3.4; 0.86/18.6/2.5 for the central gland, and 0.64/29.2/4.7; 0.71/26.3/2.2 for the peripheral zone. We provide an openly accessible, expert-annotated 3T dataset of prostate MRI and a reproducible benchmark to foster the development of prostate segmentation algorithms.

Sections du résumé

BACKGROUND
The development of deep learning (DL) models for prostate segmentation on magnetic resonance imaging (MRI) depends on expert-annotated data and reliable baselines, which are often not publicly available. This limits both reproducibility and comparability.
METHODS
Prostate158 consists of 158 expert annotated biparametric 3T prostate MRIs comprising T2w sequences and diffusion-weighted sequences with apparent diffusion coefficient maps. Two U-ResNets trained for segmentation of anatomy (central gland, peripheral zone) and suspicious lesions for prostate cancer (PCa) with a PI-RADS score of ≥4 served as baseline algorithms. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average surface distance (ASD). The Wilcoxon test with Bonferroni correction was used to evaluate differences in performance. The generalizability of the baseline model was assessed using the open datasets Medical Segmentation Decathlon and PROSTATEx.
RESULTS
Compared to Reader 1, the models achieved a DSC/HD/ASD of 0.88/18.3/2.2 for the central gland, 0.75/22.8/1.9 for the peripheral zone, and 0.45/36.7/17.4 for PCa. Compared with Reader 2, the DSC/HD/ASD were 0.88/17.5/2.6 for the central gland, 0.73/33.2/1.9 for the peripheral zone, and 0.4/39.5/19.1 for PCa. Interrater agreement measured in DSC/HD/ASD was 0.87/11.1/1.0 for the central gland, 0.75/15.8/0.74 for the peripheral zone, and 0.6/18.8/5.5 for PCa. Segmentation performances on the Medical Segmentation Decathlon and PROSTATEx were 0.82/22.5/3.4; 0.86/18.6/2.5 for the central gland, and 0.64/29.2/4.7; 0.71/26.3/2.2 for the peripheral zone.
CONCLUSIONS
We provide an openly accessible, expert-annotated 3T dataset of prostate MRI and a reproducible benchmark to foster the development of prostate segmentation algorithms.

Identifiants

DOI: 10.1016/j.compbiomed.2022.105817 PMID: 35841780
pubmed: 35841780
pii: S0010-4825(22)00578-9
doi: 10.1016/j.compbiomed.2022.105817
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

105817

Informations de copyright

Copyright © 2022 Elsevier Ltd. All rights reserved.

Auteurs

Lisa C Adams (LC)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany. Electronic address: lisa.christine.adams@gmail.com.

Marcus R Makowski (MR)

Technical University of Munich, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Ismaninger Str. 22, 81675, Munich, Germany.

Günther Engel (G)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany; Institute for Diagnostic and Interventional Radiology, Georg-August University, Göttingen, Germany.

Maximilian Rattunde (M)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany.

Felix Busch (F)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany.

Patrick Asbach (P)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany.

Stefan M Niehues (SM)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany.

Shankeeth Vinayahalingam (S)

Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, GA, the Netherlands.

Bram van Ginneken (B)

Radboud University Medical Center, Nijmegen, GA, the Netherlands.

Geert Litjens (G)

Radboud University Medical Center, Nijmegen, GA, the Netherlands.

Keno K Bressem (KK)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Prostate158 - An expert-annotated 3T MRI...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Prostate158 - An expert-annotated 3T MRI...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Prostate158 - An expert-annotated 3T MRI...
questionsmedicales.fr › Tissus › Glandes exocrines › Prostate › Prostate158 - An expert-annotated 3T MRI...
questionsmedicales.fr › Maladies urogénitales › Tumeurs de l'appareil urogénital › Tumeurs de l'appareil génital mâle › Tumeurs de la prostate › Prostate158 - An expert-annotated 3T MRI...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Méthodologie en recherche épidémiologique › Reproductibilité des résultats › Prostate158 - An expert-annotated 3T MRI...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études rétrospectives › Prostate158 - An expert-annotated 3T MRI...