Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images

Prostate Cancer (PCa) is one of the most common diseases in adult males. Currently, mp-MRI imaging represents the most promising technique for screening, diagnosing, and managing this cancer. However, the multiple mp-MRI sequences' visual interpretation is not straightforward and may present cr...

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Detalles Bibliográficos
Autores: Bouslimi, Yahya, Ben Aïcha, Takwa|||0000-0002-3786-3649, Kacem Echi, Afef|||0000-0001-9219-5228
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:276684
Acceso en línea:https://ddd.uab.cat/record/276684
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1620
Access Level:acceso abierto
Palabra clave:Computer-aided Diagnosis
Convolutional Neural Network
Magnetic Resonance Imaging
MultiResU-Net
Prostate Cancer
U-Net
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spelling Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI ImagesBouslimi, YahyaBen Aïcha, Takwa|||0000-0002-3786-3649Kacem Echi, Afef|||0000-0001-9219-5228Computer-aided DiagnosisConvolutional Neural NetworkMagnetic Resonance ImagingMultiResU-NetProstate CancerU-NetProstate Cancer (PCa) is one of the most common diseases in adult males. Currently, mp-MRI imaging represents the most promising technique for screening, diagnosing, and managing this cancer. However, the multiple mp-MRI sequences' visual interpretation is not straightforward and may present crucial inter-reader variability in the diagnosis, especially when the images contradict each other. In this work, we propose a computer-aided diagnostic system to assist the radiologist in locating and segmenting prostate lesions. As fully convolutional neural networks (UNet) have proved themselves the leading algorithm for biomedical image segmentation, we investigate their use to find PCa lesions and segment for accurate lesions contours jointly. We offer a fully automatic system via MultiResUNet, initially proposed to segment skin cancer. We trained and validated an altered version of the MultiResUnet model using an augmented Radboudumc prostate cancer dataset and obtained encouraging results. An accuracy of 98.34\% is achieved, outperforming the concurrent system based on deep architecture. 22023-01-0120232023-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/276684https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1620reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2766842026-06-06T12:50:31Z
dc.title.none.fl_str_mv Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images
title Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images
spellingShingle Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images
Bouslimi, Yahya
Computer-aided Diagnosis
Convolutional Neural Network
Magnetic Resonance Imaging
MultiResU-Net
Prostate Cancer
U-Net
title_short Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images
title_full Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images
title_fullStr Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images
title_full_unstemmed Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images
title_sort Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images
dc.creator.none.fl_str_mv Bouslimi, Yahya
Ben Aïcha, Takwa|||0000-0002-3786-3649
Kacem Echi, Afef|||0000-0001-9219-5228
author Bouslimi, Yahya
author_facet Bouslimi, Yahya
Ben Aïcha, Takwa|||0000-0002-3786-3649
Kacem Echi, Afef|||0000-0001-9219-5228
author_role author
author2 Ben Aïcha, Takwa|||0000-0002-3786-3649
Kacem Echi, Afef|||0000-0001-9219-5228
author2_role author
author
dc.subject.none.fl_str_mv Computer-aided Diagnosis
Convolutional Neural Network
Magnetic Resonance Imaging
MultiResU-Net
Prostate Cancer
U-Net
topic Computer-aided Diagnosis
Convolutional Neural Network
Magnetic Resonance Imaging
MultiResU-Net
Prostate Cancer
U-Net
description Prostate Cancer (PCa) is one of the most common diseases in adult males. Currently, mp-MRI imaging represents the most promising technique for screening, diagnosing, and managing this cancer. However, the multiple mp-MRI sequences' visual interpretation is not straightforward and may present crucial inter-reader variability in the diagnosis, especially when the images contradict each other. In this work, we propose a computer-aided diagnostic system to assist the radiologist in locating and segmenting prostate lesions. As fully convolutional neural networks (UNet) have proved themselves the leading algorithm for biomedical image segmentation, we investigate their use to find PCa lesions and segment for accurate lesions contours jointly. We offer a fully automatic system via MultiResUNet, initially proposed to segment skin cancer. We trained and validated an altered version of the MultiResUnet model using an augmented Radboudumc prostate cancer dataset and obtained encouraging results. An accuracy of 98.34\% is achieved, outperforming the concurrent system based on deep architecture.
publishDate 2023
dc.date.none.fl_str_mv 2
2023-01-01
2023
2023-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/276684
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1620
url https://ddd.uab.cat/record/276684
https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1620
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
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