U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-00692-1_28 http://hdl.handle.net/11449/210000 |
Resumo: | This paper presents a novel framework devoted to the detection of HCC (Hepato-Cellular Carcinoma) within hepatic DCE-MRI (Dynamic Contrast-Enhanced MRI) sequences, by a deep learning approach. In clinical routine, radiologists usually consider different phases during contrast injection (before injection; arterial phase; portal phase for instance) for HCC diagnosis. By employing a U-Net architecture, we are able to identify such tumors with a very high accuracy (98.5% of classification rate at best) for a small cohort of patients, which should be confirmed in future works by considering larger groups. We also show in this paper the influence of patch size for this machine learning process, and the positive impact of employing all phases available in DCE-MRI sequences, compared to use only one. |
id |
UNSP_371df1c653d2f139dfc998a383168560 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/210000 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net FrameworkThis paper presents a novel framework devoted to the detection of HCC (Hepato-Cellular Carcinoma) within hepatic DCE-MRI (Dynamic Contrast-Enhanced MRI) sequences, by a deep learning approach. In clinical routine, radiologists usually consider different phases during contrast injection (before injection; arterial phase; portal phase for instance) for HCC diagnosis. By employing a U-Net architecture, we are able to identify such tumors with a very high accuracy (98.5% of classification rate at best) for a small cohort of patients, which should be confirmed in future works by considering larger groups. We also show in this paper the influence of patch size for this machine learning process, and the positive impact of employing all phases available in DCE-MRI sequences, compared to use only one.Lodz University of Technology, Faculty of Electrical, Electronic, Computer and Control EngineeringLodz Univ Technol, Inst Appl Comp Sci, 18-22 Stefanowskiego St, PL-90924 Lodz, PolandUniv Clermont Auvergne, SIGMA Clermont, CNRS, Inst Pascal, F-63000 Clermont Ferrand, FranceCtr Hosp Univ, Clermont Ferrand, FranceSao Paulo State Univ, Dept Phys & Biophys, Botucatu, SP, BrazilSao Paulo State Univ, Dept Phys & Biophys, Botucatu, SP, BrazilLodz University of Technology, Faculty of Electrical, Electronic, Computer and Control Engineering: 501/12-24-1-5428SpringerLodz Univ TechnolUniv Clermont AuvergneCtr Hosp UnivUniversidade Estadual Paulista (Unesp)Fabijanska, AnnaVacavant, AntoineLebre, Marie-AngePavan, Ana L. M. [UNESP]Pina, Diana R. de [UNESP]Abergel, ArmandChabrot, PascalMagnin, BenoitChmielewski, L. J.Kozera, R.Orlowski, A.Wojciechowski, K.Bruckstein, A. M.Petkov, N.2021-06-25T12:36:20Z2021-06-25T12:36:20Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject319-328http://dx.doi.org/10.1007/978-3-030-00692-1_28Computer Vision And Graphics ( Iccvg 2018). Cham: Springer International Publishing Ag, v. 11114, p. 319-328, 2018.0302-9743http://hdl.handle.net/11449/21000010.1007/978-3-030-00692-1_28WOS:000614368800028Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputer Vision And Graphics ( Iccvg 2018)info:eu-repo/semantics/openAccess2021-10-23T19:50:12Zoai:repositorio.unesp.br:11449/210000Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:23:24.179385Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework |
title |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework |
spellingShingle |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework Fabijanska, Anna |
title_short |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework |
title_full |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework |
title_fullStr |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework |
title_full_unstemmed |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework |
title_sort |
U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework |
author |
Fabijanska, Anna |
author_facet |
Fabijanska, Anna Vacavant, Antoine Lebre, Marie-Ange Pavan, Ana L. M. [UNESP] Pina, Diana R. de [UNESP] Abergel, Armand Chabrot, Pascal Magnin, Benoit Chmielewski, L. J. Kozera, R. Orlowski, A. Wojciechowski, K. Bruckstein, A. M. Petkov, N. |
author_role |
author |
author2 |
Vacavant, Antoine Lebre, Marie-Ange Pavan, Ana L. M. [UNESP] Pina, Diana R. de [UNESP] Abergel, Armand Chabrot, Pascal Magnin, Benoit Chmielewski, L. J. Kozera, R. Orlowski, A. Wojciechowski, K. Bruckstein, A. M. Petkov, N. |
author2_role |
author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Lodz Univ Technol Univ Clermont Auvergne Ctr Hosp Univ Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Fabijanska, Anna Vacavant, Antoine Lebre, Marie-Ange Pavan, Ana L. M. [UNESP] Pina, Diana R. de [UNESP] Abergel, Armand Chabrot, Pascal Magnin, Benoit Chmielewski, L. J. Kozera, R. Orlowski, A. Wojciechowski, K. Bruckstein, A. M. Petkov, N. |
description |
This paper presents a novel framework devoted to the detection of HCC (Hepato-Cellular Carcinoma) within hepatic DCE-MRI (Dynamic Contrast-Enhanced MRI) sequences, by a deep learning approach. In clinical routine, radiologists usually consider different phases during contrast injection (before injection; arterial phase; portal phase for instance) for HCC diagnosis. By employing a U-Net architecture, we are able to identify such tumors with a very high accuracy (98.5% of classification rate at best) for a small cohort of patients, which should be confirmed in future works by considering larger groups. We also show in this paper the influence of patch size for this machine learning process, and the positive impact of employing all phases available in DCE-MRI sequences, compared to use only one. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 2021-06-25T12:36:20Z 2021-06-25T12:36:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-00692-1_28 Computer Vision And Graphics ( Iccvg 2018). Cham: Springer International Publishing Ag, v. 11114, p. 319-328, 2018. 0302-9743 http://hdl.handle.net/11449/210000 10.1007/978-3-030-00692-1_28 WOS:000614368800028 |
url |
http://dx.doi.org/10.1007/978-3-030-00692-1_28 http://hdl.handle.net/11449/210000 |
identifier_str_mv |
Computer Vision And Graphics ( Iccvg 2018). Cham: Springer International Publishing Ag, v. 11114, p. 319-328, 2018. 0302-9743 10.1007/978-3-030-00692-1_28 WOS:000614368800028 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computer Vision And Graphics ( Iccvg 2018) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
319-328 |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128641704394752 |