Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome

Treball fi de màster de: Master in Computational Biomedical Engineering

Detalles Bibliográficos
Autor: Perera Bel, Enric
Tipo de recurso: tesis de maestría
Fecha de publicación:2017
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:10230/33180
Acceso en línea:http://hdl.handle.net/10230/33180
Access Level:acceso abierto
Palabra clave:Fetus -- Malalties
Diagnòstic prenatal
Placenta segmentation
Vessel segmentation
Random walker algorithm
GPU optimization
Medical application
Tutors: Miguel Ángel González Ballester i Mario Ceresa
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spelling Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndromePerera Bel, EnricFetus -- MalaltiesDiagnòstic prenatalPlacenta segmentationVessel segmentationRandom walker algorithmGPU optimizationMedical applicationTutors: Miguel Ángel González Ballester i Mario CeresaTreball fi de màster de: Master in Computational Biomedical EngineeringIn this work we present a placenta and vessel segmentation method for a medical application for Twin-to-Twin Transfusion Syndrome (TTTS). TTTS is a fetal disease that occurs in twin monochorionic pregnancies and can be fatal if left untreated. Right now it is treated with fetoscopic laser coagulation. This method highly improves prognosis, but still presents some risks since the intervention is critical in order to avoid abortion risks. Therefore, it can benefit from image segmentation techniques for surgery planning and guidance. Placenta segmentation is not easy due to a high variability on its location and shape, thus semiautomatic methods are the ones that have shown better results for ultrasound (US) segmentation. We implement one of them, the random walker (RW) algorithm, and include it in a graphic user interface for medical use. Thirty-one sets of US and Doppler US images were available in this study, but four are discarded due to poor gradient quality between tissues. Individual segmentation of placenta and vessel from different images is performed (US and Doppler US, respectively), as well as combined in a multi-label segmentation (Doppler US). The implemented method is compared with previous studies, and it is modified in order to accelerate its computation using a graphics processing unit (GPU). We show that this algorithm offers a fine boundary adherence for US images for both placenta and vessel segmentation, mostly in regions with high tissue gradients, but it is dependent and sensitive on the protocol followed for the manual initialization, which is in concordance with the literature study. We also observe that single and multiple segmentation show similar segmentation results, mostly in vessel and not so much in placenta. The GPU implementation shows faster computation rates, but needs of more iterations to converge to a solution, compared to the already optimized CPU implementation. However, using a high end graphics card accelerates the overall computation, while there is still room for improvement. The RW algorithm had already been used for placenta segmentation and we have validated its accuracy. However, there does not exist a gold standard in this procedure so we plan on including more methods in the medical application, so the clinician can choose the approach that fits the best to each anatomy and image characteristics. In this project we tightly collaborate with BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine Hospital Clínic and Hospital Sant Joan de Déu, Universitat de Barcelona. We aim to create a surgery planning and tracking tool that can be used to improve fetoscopic laser coagulation prognosis and, later, that can be extended to other surgeries.201720172017info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/33180reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésAtribución-NoComercial-SinDerivadas 3.0 EspañaAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/331802026-05-29T05:05:01Z
dc.title.none.fl_str_mv Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
title Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
spellingShingle Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
Perera Bel, Enric
Fetus -- Malalties
Diagnòstic prenatal
Placenta segmentation
Vessel segmentation
Random walker algorithm
GPU optimization
Medical application
Tutors: Miguel Ángel González Ballester i Mario Ceresa
title_short Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
title_full Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
title_fullStr Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
title_full_unstemmed Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
title_sort Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
dc.creator.none.fl_str_mv Perera Bel, Enric
author Perera Bel, Enric
author_facet Perera Bel, Enric
author_role author
dc.subject.none.fl_str_mv Fetus -- Malalties
Diagnòstic prenatal
Placenta segmentation
Vessel segmentation
Random walker algorithm
GPU optimization
Medical application
Tutors: Miguel Ángel González Ballester i Mario Ceresa
topic Fetus -- Malalties
Diagnòstic prenatal
Placenta segmentation
Vessel segmentation
Random walker algorithm
GPU optimization
Medical application
Tutors: Miguel Ángel González Ballester i Mario Ceresa
description Treball fi de màster de: Master in Computational Biomedical Engineering
publishDate 2017
dc.date.none.fl_str_mv 2017
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/33180
url http://hdl.handle.net/10230/33180
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
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 15,812429

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