Cargando…

Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model

BACKGROUND: Due to the large variability in the prostate gland of different patient groups, manual segmentation is time-consuming and subject to inter-and intra-reader variations. Hence, we propose a U-Net model to automatically segment the prostate and its zones, including the peripheral zone (PZ),...

Descripción completa

Detalles Bibliográficos
Autores principales: Rezaeijo, Seyed Masoud, Jafarpoor Nesheli, Shabnam, Fatan Serj, Mehdi, Tahmasebi Birgani, Mohammad Javad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511435/
https://www.ncbi.nlm.nih.gov/pubmed/36185056
http://dx.doi.org/10.21037/qims-22-115
_version_ 1784797641949839360
author Rezaeijo, Seyed Masoud
Jafarpoor Nesheli, Shabnam
Fatan Serj, Mehdi
Tahmasebi Birgani, Mohammad Javad
author_facet Rezaeijo, Seyed Masoud
Jafarpoor Nesheli, Shabnam
Fatan Serj, Mehdi
Tahmasebi Birgani, Mohammad Javad
author_sort Rezaeijo, Seyed Masoud
collection PubMed
description BACKGROUND: Due to the large variability in the prostate gland of different patient groups, manual segmentation is time-consuming and subject to inter-and intra-reader variations. Hence, we propose a U-Net model to automatically segment the prostate and its zones, including the peripheral zone (PZ), transitional zone (TZ), anterior fibromuscular stroma (AFMS), and urethra on the MRI [T2-weighted (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC)], and multimodality image fusion. METHODS: A total of 91 eligible patients were retrospectively identified; 50 patients were considered for training process in a 10-fold cross-validation fashion and 41 ones for external test. Firstly, images were registered, and cropping was performed through a bounding box. In addition to T2W, DWI, and ADC separately, fused images were used. We considered three combinations, including T2W + DWI, T2W + ADC, and DWI + ADC, using wavelet transform. U-Net was applied to segment the prostate and its zones, AFMS, and urethra in a 10-fold cross-validation fashion. Eventually, dice score (DSC), intersection over union (IoU), precision, recall, and Hausdorff distance (HD) were used to evaluate the proposed model. RESULTS: Using T2W images alone on the external test images, higher DSC, IoU, precision, and recall was achieved than the individual DWI and ADC images. DSC of 95%, 94%,98%, 94%, and 88%, IoU of 88%, 88.5%, 96%, 90%, and 79%, precision of 95.9%, 93.9%, 97.6%, 93.83%, and 87.82%, and recall of 94.2%, 94.2%, 98.3%, 94%, 87.93% was achieved for the whole prostate, PZ, TZ, urethra, and AFMS, respectively. The results clearly show that the best segmentation was obtained when the model is trained using T2W + DWI images. DSC of 99.06%, 99,05%, 99.04%, 99.09%, and 98.08%, IoU of 97.09%, 97.02%, 98.12%, 98.13%, and 96%, precision of 99.24%, 98.22%, 98.91%, 99.23%, and 98.9%, and recall of 98.3%, 99.8%, 99.02%, 98.93%, and 97.51% was achieved for the whole prostate, PZ, TZ, urethra, and AFMS, respectively. The min of the HD in the testing set for three combinations was 0.29 for the T2W + ADC procedure in the whole prostate class. CONCLUSIONS: Better performance was achieved using T2W + DWI images than T2W, DWI, and ADC separately or T2W + ADC and DWI + ADC in combination.
format Online
Article
Text
id pubmed-9511435
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher AME Publishing Company
record_format MEDLINE/PubMed
spelling pubmed-95114352022-10-01 Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model Rezaeijo, Seyed Masoud Jafarpoor Nesheli, Shabnam Fatan Serj, Mehdi Tahmasebi Birgani, Mohammad Javad Quant Imaging Med Surg Original Article BACKGROUND: Due to the large variability in the prostate gland of different patient groups, manual segmentation is time-consuming and subject to inter-and intra-reader variations. Hence, we propose a U-Net model to automatically segment the prostate and its zones, including the peripheral zone (PZ), transitional zone (TZ), anterior fibromuscular stroma (AFMS), and urethra on the MRI [T2-weighted (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC)], and multimodality image fusion. METHODS: A total of 91 eligible patients were retrospectively identified; 50 patients were considered for training process in a 10-fold cross-validation fashion and 41 ones for external test. Firstly, images were registered, and cropping was performed through a bounding box. In addition to T2W, DWI, and ADC separately, fused images were used. We considered three combinations, including T2W + DWI, T2W + ADC, and DWI + ADC, using wavelet transform. U-Net was applied to segment the prostate and its zones, AFMS, and urethra in a 10-fold cross-validation fashion. Eventually, dice score (DSC), intersection over union (IoU), precision, recall, and Hausdorff distance (HD) were used to evaluate the proposed model. RESULTS: Using T2W images alone on the external test images, higher DSC, IoU, precision, and recall was achieved than the individual DWI and ADC images. DSC of 95%, 94%,98%, 94%, and 88%, IoU of 88%, 88.5%, 96%, 90%, and 79%, precision of 95.9%, 93.9%, 97.6%, 93.83%, and 87.82%, and recall of 94.2%, 94.2%, 98.3%, 94%, 87.93% was achieved for the whole prostate, PZ, TZ, urethra, and AFMS, respectively. The results clearly show that the best segmentation was obtained when the model is trained using T2W + DWI images. DSC of 99.06%, 99,05%, 99.04%, 99.09%, and 98.08%, IoU of 97.09%, 97.02%, 98.12%, 98.13%, and 96%, precision of 99.24%, 98.22%, 98.91%, 99.23%, and 98.9%, and recall of 98.3%, 99.8%, 99.02%, 98.93%, and 97.51% was achieved for the whole prostate, PZ, TZ, urethra, and AFMS, respectively. The min of the HD in the testing set for three combinations was 0.29 for the T2W + ADC procedure in the whole prostate class. CONCLUSIONS: Better performance was achieved using T2W + DWI images than T2W, DWI, and ADC separately or T2W + ADC and DWI + ADC in combination. AME Publishing Company 2022-10 /pmc/articles/PMC9511435/ /pubmed/36185056 http://dx.doi.org/10.21037/qims-22-115 Text en 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Rezaeijo, Seyed Masoud
Jafarpoor Nesheli, Shabnam
Fatan Serj, Mehdi
Tahmasebi Birgani, Mohammad Javad
Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model
title Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model
title_full Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model
title_fullStr Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model
title_full_unstemmed Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model
title_short Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model
title_sort segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the mris and multimodality image fusion using u-net model
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511435/
https://www.ncbi.nlm.nih.gov/pubmed/36185056
http://dx.doi.org/10.21037/qims-22-115
work_keys_str_mv AT rezaeijoseyedmasoud segmentationoftheprostateitszonesanteriorfibromuscularstromaandurethraonthemrisandmultimodalityimagefusionusingunetmodel
AT jafarpoorneshelishabnam segmentationoftheprostateitszonesanteriorfibromuscularstromaandurethraonthemrisandmultimodalityimagefusionusingunetmodel
AT fatanserjmehdi segmentationoftheprostateitszonesanteriorfibromuscularstromaandurethraonthemrisandmultimodalityimagefusionusingunetmodel
AT tahmasebibirganimohammadjavad segmentationoftheprostateitszonesanteriorfibromuscularstromaandurethraonthemrisandmultimodalityimagefusionusingunetmodel