Health AI Central
Peer-Reviewed Publication
Eur Radiol2023;33(7):5087-5096.July 1, 2023Journal Article

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

Ana Jimenez-Pastor1, Rafael Lopez-Gonzalez2,3, Belén Fos-Guarinos2, Fabio Garcia-Castro2, Mark Wittenberg2, Asunción Torregrosa-Andrés4, Luis Marti-Bonmati4, Margarita Garcia-Fontes5, Pablo Duarte5, Juan Pablo Gambini5, Leonardo Kayat Bittencourt6,7, Felipe Campos Kitamura7, Vasantha Kumar Venugopal8, Vidur Mahajan8, Pablo Ros9, Emilio Soria-Olivas3, Angel Alberich-Bayarri2
1Quantitative Imaging Biomarkers in Medicine (Quibim S.L.), Aragon Avenue, 30, 13th floor, Office I-J, 46021, Valencia, Spain. anajimenez@quibim.com.
2Quantitative Imaging Biomarkers in Medicine (Quibim S.L.), Aragon Avenue, 30, 13th floor, Office I-J, 46021, Valencia, Spain.
3Intelligent Data Analysis Laboratory (IDAL), ETSE, Universidad de Valencia, Valencia, Spain.
4Department of Radiology and GIBI230 Research Group On Biomedical Imaging, Hospital Universitario Y Politécnico de La Fe and Instituto de Investigación Sanitaria La Fe, Valencia, Spain.
5Centro Uruguayo de Imagenologia Molecular (CUDIM), Montevideo, Uruguay.
6University Hospitals Cleveland Medical Center, Cleveland, USA.
7DasaInova, Dasa, Unifesp, São Paulo, Brazil.
8Mahajan Imaging, New Delhi, India.
9Stony Brook University, Stony Brook, NY, USA.
PubMed DOI

Abstract

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 foll…

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