Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation-Reference-Cited by-同舟云学术

Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation

Published:2022-01-10 Issue:5 Volume:32 Page:3248-3259
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Rouvière Olivier^ORCID,Moldovan Paul Cezar,Vlachomitrou Anna,Gouttard Sylvain,Riche Benjamin,Groth Alexandra,Rabotnikov Mark,Ruffion Alain,Colombel Marc,Crouzet Sébastien,Weese Juergen,Rabilloud Muriel

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-021-08408-5.pdf

Reference47 articles.

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2. Lee DK, Sung DJ, Kim CS et al (2020) Three-dimensional convolutional neural network for prostate MRI segmentation and comparison of prostate volume measurements by use of artificial neural network and ellipsoid formula. AJR Am J Roentgenol 214:1229–1238

3. Penzkofer T, Padhani AR, Turkbey B et al (2021) ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging. Eur Radiol. https://doi.org/10.1007/s00330-021-08021-6

4. Schelb P, Wang X, Radtke JP et al (2021) Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment. Eur Radiol 31:302–313

5. Turkbey B, Rosenkrantz AB, Haider MA et al (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol 76:340–351

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