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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Sensors (Basel, Switzerland) Ročník 21; číslo 8; s. 2709
Hlavní autori: Lai, Chih-Ching, Wang, Hsin-Kai, Wang, Fu-Nien, Peng, Yu-Ching, Lin, Tzu-Ping, Peng, Hsu-Hsia, Shen, Shu-Huei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 12.04.2021
MDPI
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s21082709