Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks

The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in detecting PCa. However, prostate MRI assessment re...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 21; no. 8; p. 2709
Main Authors:	Lai, Chih-Ching, Wang, Hsin-Kai, Wang, Fu-Nien, Peng, Yu-Ching, Lin, Tzu-Ping, Peng, Hsu-Hsia, Shen, Shu-Huei
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 12.04.2021 MDPI
Subjects:	Accuracy ADC Datasets DCNN Deep Learning Diffusion Magnetic Resonance Imaging encoder–decoder architecture Humans Machine learning Magnetic Resonance Imaging Male Methods Multiparametric Magnetic Resonance Imaging Neural networks Neural Networks, Computer Patients Prostate - diagnostic imaging Prostate cancer Prostatic Neoplasms - diagnostic imaging SegNet Semantics T2W zonal segmentation ADC DCNN zonal segmentation DWI T2W SegNet encoder–decoder architecture
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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