Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.

Recent advances in fetal magnetic resonance imaging (MRI) open the door to improved detection and characterization of fetal and placental abnormalities. Since interpreting MRI data can be complex and ambiguous, there is a need for robust computational methods able to quantify placental anatomy (incl...

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Autores: Torrents-Barrena J, Piella G, Masoller N, Gratacós E, Eixarch E, Ceresa M, González Ballester MÁ
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p16143
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=16143
Access Level:acceso abierto
Palabra clave:*3D Super-resolution
*Corner detector
*Fetal surgery
*Gabor filter
*MRI
*Placenta and blood vessels segmentation
*Support vector machine
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spelling Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.Torrents-Barrena JPiella GMasoller NGratacós EEixarch ECeresa MGonzález Ballester MÁ*3D Super-resolution*Corner detector*Fetal surgery*Gabor filter*MRI*Placenta and blood vessels segmentation*Support vector machineRecent advances in fetal magnetic resonance imaging (MRI) open the door to improved detection and characterization of fetal and placental abnormalities. Since interpreting MRI data can be complex and ambiguous, there is a need for robust computational methods able to quantify placental anatomy (including its vasculature) and function. In this work, we propose a novel fully-automated method to segment the placenta and its peripheral blood vessels from fetal MRI. First, a super-resolution reconstruction of the uterus is generated by combining axial, sagittal and coronal views. The placenta is then segmented using 3D Gabor filters, texture features and Support Vector Machines. A uterus edge-based instance selection is proposed to identify the support vectors defining the placenta boundary. Subsequently, peripheral blood vessels are extracted through a curvature-based corner detector. Our approach is validated on a rich set of 44 control and pathological cases: singleton and (normal / monochorionic) twin pregnancies between 25-37 weeks of gestation. Dice coefficients of 0.82 ?±? 0.02 and 0.81 ?±? 0.08 are achieved for placenta and its vasculature segmentation, respectively. A comparative analysis with state of the art convolutional neural networks (CNN), namely, 3D U-Net, V-Net, DeepMedic, Holistic3D Net, HighRes3D Net and Dense V-Net is also conducted for placenta localization, with our method outperforming all CNN approaches. Results suggest that our methodology can aid the diagnosis and surgical planning of severe fetal disorders.ELSEVIER2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=16143MEDICAL IMAGE ANALYSISISSN: 13618415ISSNe: 13618423reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p161432026-05-27T12:37:41Z
dc.title.none.fl_str_mv Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
title Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
spellingShingle Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
Torrents-Barrena J
*3D Super-resolution
*Corner detector
*Fetal surgery
*Gabor filter
*MRI
*Placenta and blood vessels segmentation
*Support vector machine
title_short Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
title_full Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
title_fullStr Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
title_full_unstemmed Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
title_sort Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.
dc.creator.none.fl_str_mv Torrents-Barrena J
Piella G
Masoller N
Gratacós E
Eixarch E
Ceresa M
González Ballester MÁ
author Torrents-Barrena J
author_facet Torrents-Barrena J
Piella G
Masoller N
Gratacós E
Eixarch E
Ceresa M
González Ballester MÁ
author_role author
author2 Piella G
Masoller N
Gratacós E
Eixarch E
Ceresa M
González Ballester MÁ
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv *3D Super-resolution
*Corner detector
*Fetal surgery
*Gabor filter
*MRI
*Placenta and blood vessels segmentation
*Support vector machine
topic *3D Super-resolution
*Corner detector
*Fetal surgery
*Gabor filter
*MRI
*Placenta and blood vessels segmentation
*Support vector machine
description Recent advances in fetal magnetic resonance imaging (MRI) open the door to improved detection and characterization of fetal and placental abnormalities. Since interpreting MRI data can be complex and ambiguous, there is a need for robust computational methods able to quantify placental anatomy (including its vasculature) and function. In this work, we propose a novel fully-automated method to segment the placenta and its peripheral blood vessels from fetal MRI. First, a super-resolution reconstruction of the uterus is generated by combining axial, sagittal and coronal views. The placenta is then segmented using 3D Gabor filters, texture features and Support Vector Machines. A uterus edge-based instance selection is proposed to identify the support vectors defining the placenta boundary. Subsequently, peripheral blood vessels are extracted through a curvature-based corner detector. Our approach is validated on a rich set of 44 control and pathological cases: singleton and (normal / monochorionic) twin pregnancies between 25-37 weeks of gestation. Dice coefficients of 0.82 ?±? 0.02 and 0.81 ?±? 0.08 are achieved for placenta and its vasculature segmentation, respectively. A comparative analysis with state of the art convolutional neural networks (CNN), namely, 3D U-Net, V-Net, DeepMedic, Holistic3D Net, HighRes3D Net and Dense V-Net is also conducted for placenta localization, with our method outperforming all CNN approaches. Results suggest that our methodology can aid the diagnosis and surgical planning of severe fetal disorders.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=16143
url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=16143
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv ELSEVIER
publisher.none.fl_str_mv ELSEVIER
dc.source.none.fl_str_mv MEDICAL IMAGE ANALYSIS
ISSN: 13618415
ISSNe: 13618423
reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
instname:Fundació Sant Joan de Déu
instname_str Fundació Sant Joan de Déu
reponame_str r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
collection r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
repository.name.fl_str_mv
repository.mail.fl_str_mv
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