Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality

• Published in: Journal of magnetic resonance imaging Vol. 55; no. 2; pp. 480 - 490
• Main Authors: Cipollari, Stefano, Guarrasi, Valerio, Pecoraro, Martina, Bicchetti, Marco, Messina, Emanuele, Farina, Lorenzo, Paci, Paola, Catalano, Carlo, Panebianco, Valeria
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2022; Wiley Subscription Services, Inc
• Subjects: Accuracy; Aged; Algorithms; artificial intelligence; Artificial neural networks; Automation; Classification; deep learning; Diffusion Magnetic Resonance Imaging; Field strength; Humans; Identification methods; Image classification; Image quality; Magnetic Resonance Imaging; Male; Medical imaging; Multiparametric Magnetic Resonance Imaging; multiparametric MRI; Neural networks; Neural Networks, Computer; Prostate; Prostate - diagnostic imaging; Prostate cancer; Prostatic Neoplasms - diagnostic imaging; quality control; Resonance; Retrospective Studies; Sequence analysis; Sequences; Statistical analysis; Statistical tests; multiparametric MRI; deep learning; prostate cancer; quality control; artificial intelligence

Background Prostate magnetic resonance imaging (MRI) is technically demanding, requiring high image quality to reach its full diagnostic potential. An automated method to identify diagnostically inadequate images could help optimize image quality. Purpose To develop a convolutional neural networks (...