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Objective: Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines. Therefore, the main objective of this work was to develop a robust and reproducible CNN-based automatic prostate multi-regional segmentation model using an intercontinental cohort of prostate MRI.

Methods: A heterogeneous database of 243 T2-weighted prostate studies from 7 countries and 10 machines of 3 different vendors, with the CZ-TZ, PZ, and SV regions manually delineated by two experienced radiologists (ground truth), was used to train (n = 123) and test (n = 120) a U-Net-based model with deep supervision using a cyclical learning rate. The performance of the model was evaluated by means of dice similarity coefficient (DSC), among others. Segmentation results with a DSC above 0.7 were considered accurate.

Results: The proposed method obtained a DSC of 0.88 ± 0.01, 0.85 ± 0.02, 0.72 ± 0.02, and 0.72 ± 0.02 for the prostate gland, CZ-TZ, PZ, and SV respectively in the 120 studies of the test set when comparing the predicted segmentations with the ground truth. No statistically significant differences were found in the results obtained between manufacturers or continents.

Conclusion: Prostate multi-regional T2-weighted MR images automatic segmentation can be accurately achieved by U-Net like CNN, generalizable in a highly variable clinical environment with different equipment, acquisition configurations, and population.

Key Points: • Deep learning techniques allows the accurate segmentation of the prostate in three different regions on MR T2w images. • Multi-centric database proved the generalization of the CNN model on different institutions across different continents. • CNN models can be used to aid on the diagnosis and follow-up of patients with prostate cancer.

Titel:	Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.
Autor/in / Beteiligte Person:	Jimenez-Pastor, A ; Lopez-Gonzalez, R ; Fos-Guarinos, B ; Garcia-Castro, F ; Wittenberg, M ; Torregrosa-Andrés, A ; Marti-Bonmati, L ; Garcia-Fontes, M ; Duarte, P ; Gambini, JP ; Bittencourt, LK ; Kitamura, FC ; Venugopal, VK ; Mahajan, V ; Ros, P ; Soria-Olivas, E ; Alberich-Bayarri, A
Link:	Full Text Finder (Volltext)
Zeitschrift:	European radiology, Jg. 33 (2023-07-01), Heft 7, S. 5087-5096
Veröffentlichung:	Berlin : Springer International, c1991-, 2023
Medientyp:	academicJournal
ISSN:	1432-1084 (electronic)
DOI:	10.1007/s00330-023-09410-9
Schlagwort:	Male Humans Prostate diagnostic imaging Prostate pathology Neural Networks, Computer Magnetic Resonance Spectroscopy Image Processing, Computer-Assisted methods Magnetic Resonance Imaging methods Prostatic Neoplasms diagnostic imaging Prostatic Neoplasms pathology
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Eur Radiol] 2023 Jul; Vol. 33 (7), pp. 5087-5096. <i>Date of Electronic Publication: </i>2023 Jan 24. MeSH Terms: Magnetic Resonance Imaging* / methods ; Prostatic Neoplasms* / diagnostic imaging ; Prostatic Neoplasms* / pathology ; Male ; Humans ; Prostate / diagnostic imaging ; Prostate / pathology ; Neural Networks, Computer ; Magnetic Resonance Spectroscopy ; Image Processing, Computer-Assisted / methods References: De Visschere P (2018) Improving the diagnosis of clinically significant prostate cancer with magnetic resonance imaging. J Belg Soc Radiol 102(1):22. (PMID: 10.5334/jbsr.14386095051) ; Cui T, Kovell RC, Terlecki RP (2016) Is it time to abandon the digital rectal examination? Lessons from the PLCO Cancer Screening Trial and peer-reviewed literature. Curr Med Res Opin 32(10):1663–1669. (PMID: 10.1080/03007995.2016.119831227264113) ; Mayo Clinic (2019) PSA test. 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(PMID: 10.2214/AJR.19.2225432208009) ; Khan Z, Yahya N, Alsaih K, Ali SSA, Meriaudeau F (2020) Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI. Sensors (Basel) 20(11):3183. (PMID: 10.3390/s2011318332503330) Contributed Indexing: Keywords: Artificial intelligence; Deep learning; Diagnosis computer-assisted; Magnetic resonance imaging; Prostate Entry Date(s): Date Created: 20230123 Date Completed: 20230626 Latest Revision: 20230626 Update Code: 20240513

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Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.