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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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Autores principales: Lai, Chih-Ching, Wang, Hsin-Kai, Wang, Fu-Nien, Peng, Yu-Ching, Lin, Tzu-Ping, Peng, Hsu-Hsia, Shen, Shu-Huei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070192/
https://www.ncbi.nlm.nih.gov/pubmed/33921451
http://dx.doi.org/10.3390/s21082709
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author Lai, Chih-Ching
Wang, Hsin-Kai
Wang, Fu-Nien
Peng, Yu-Ching
Lin, Tzu-Ping
Peng, Hsu-Hsia
Shen, Shu-Huei
author_facet Lai, Chih-Ching
Wang, Hsin-Kai
Wang, Fu-Nien
Peng, Yu-Ching
Lin, Tzu-Ping
Peng, Hsu-Hsia
Shen, Shu-Huei
author_sort Lai, Chih-Ching
collection PubMed
description 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 relies on human experts and specialized training with considerable inter-reader variability. Deep learning may be a more robust approach for prostate MRI assessment. Here we present a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model. We used PROSTATEx dataset to train the model and combined different sequences into three channels of a single image. For each subject, all slices that contained the transition zone (TZ), peripheral zone (PZ), and PCa region were selected. The datasets were produced using different combinations of images, including T2-weighted (T2W) images, diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) images. Among these groups, the T2W + DWI + ADC images exhibited the best performance with a dice similarity coefficient of 90.45% for the TZ, 70.04% for the PZ, and 52.73% for the PCa region. Image sequence analysis with a DCNN model has the potential to assist PCa diagnosis.
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spelling pubmed-80701922021-04-26 Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks Lai, Chih-Ching Wang, Hsin-Kai Wang, Fu-Nien Peng, Yu-Ching Lin, Tzu-Ping Peng, Hsu-Hsia Shen, Shu-Huei Sensors (Basel) Article 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 relies on human experts and specialized training with considerable inter-reader variability. Deep learning may be a more robust approach for prostate MRI assessment. Here we present a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model. We used PROSTATEx dataset to train the model and combined different sequences into three channels of a single image. For each subject, all slices that contained the transition zone (TZ), peripheral zone (PZ), and PCa region were selected. The datasets were produced using different combinations of images, including T2-weighted (T2W) images, diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) images. Among these groups, the T2W + DWI + ADC images exhibited the best performance with a dice similarity coefficient of 90.45% for the TZ, 70.04% for the PZ, and 52.73% for the PCa region. Image sequence analysis with a DCNN model has the potential to assist PCa diagnosis. MDPI 2021-04-12 /pmc/articles/PMC8070192/ /pubmed/33921451 http://dx.doi.org/10.3390/s21082709 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lai, Chih-Ching
Wang, Hsin-Kai
Wang, Fu-Nien
Peng, Yu-Ching
Lin, Tzu-Ping
Peng, Hsu-Hsia
Shen, Shu-Huei
Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks
title Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks
title_full Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks
title_fullStr Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks
title_full_unstemmed Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks
title_short Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks
title_sort autosegmentation of prostate zones and cancer regions from biparametric magnetic resonance images by using deep-learning-based neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070192/
https://www.ncbi.nlm.nih.gov/pubmed/33921451
http://dx.doi.org/10.3390/s21082709
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