Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is...

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Autores: López-Linares, Karen, Aranjuelo, Nerea, Kabongo, Luis, Maclair, Gregory, Lete, Nerea, Ceresa, Mario, García-Familiar, Ainhoa, Macía, Iván, González Ballester, Miguel Ángel, 1973-
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2018
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/47919
Acceso en línea:http://hdl.handle.net/10230/47919
http://dx.doi.org/10.1016/j.media.2018.03.010
Access Level:acceso abierto
Palabra clave:AAA
EVAR
Segmentation
DCNN
Deep learning
Thrombus
Post-operative
Detection
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spelling Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural NetworksLópez-Linares, KarenAranjuelo, NereaKabongo, LuisMaclair, GregoryLete, NereaCeresa, MarioGarcía-Familiar, AinhoaMacía, IvánGonzález Ballester, Miguel Ángel, 1973-AAAEVARSegmentationDCNNDeep learningThrombusPost-operativeDetectionComputerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases.This work has been supported by the DEFENSE (TIN2013-47913-C3) research project, funded by the Ministry of Economy and Competitiveness-Government of Spain.Elsevier202120212018info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/47919http://dx.doi.org/10.1016/j.media.2018.03.010reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésMedical Image Analysis. 2018;46:202-14info:eu-repo/grantAgreement/ES/1PE/TIN2013-47913-C3© Elsevier http://dx.doi.org/10.1016/j.media.2018.03.010info:eu-repo/semantics/openAccessoai:recercat.cat:10230/479192026-05-29T05:05:01Z
dc.title.none.fl_str_mv Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks
title Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks
spellingShingle Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks
López-Linares, Karen
AAA
EVAR
Segmentation
DCNN
Deep learning
Thrombus
Post-operative
Detection
title_short Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks
title_full Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks
title_fullStr Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks
title_full_unstemmed Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks
title_sort Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks
dc.creator.none.fl_str_mv López-Linares, Karen
Aranjuelo, Nerea
Kabongo, Luis
Maclair, Gregory
Lete, Nerea
Ceresa, Mario
García-Familiar, Ainhoa
Macía, Iván
González Ballester, Miguel Ángel, 1973-
author López-Linares, Karen
author_facet López-Linares, Karen
Aranjuelo, Nerea
Kabongo, Luis
Maclair, Gregory
Lete, Nerea
Ceresa, Mario
García-Familiar, Ainhoa
Macía, Iván
González Ballester, Miguel Ángel, 1973-
author_role author
author2 Aranjuelo, Nerea
Kabongo, Luis
Maclair, Gregory
Lete, Nerea
Ceresa, Mario
García-Familiar, Ainhoa
Macía, Iván
González Ballester, Miguel Ángel, 1973-
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv AAA
EVAR
Segmentation
DCNN
Deep learning
Thrombus
Post-operative
Detection
topic AAA
EVAR
Segmentation
DCNN
Deep learning
Thrombus
Post-operative
Detection
description Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases.
publishDate 2018
dc.date.none.fl_str_mv 2018
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/47919
http://dx.doi.org/10.1016/j.media.2018.03.010
url http://hdl.handle.net/10230/47919
http://dx.doi.org/10.1016/j.media.2018.03.010
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Medical Image Analysis. 2018;46:202-14
info:eu-repo/grantAgreement/ES/1PE/TIN2013-47913-C3
dc.rights.none.fl_str_mv © Elsevier http://dx.doi.org/10.1016/j.media.2018.03.010
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Elsevier http://dx.doi.org/10.1016/j.media.2018.03.010
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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