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

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dc.contributor.author Perera Bel, Enric
dc.date.accessioned 2017-11-09T10:04:03Z
dc.date.available 2017-11-09T10:04:03Z
dc.date.issued 2017-07
dc.identifier.uri http://hdl.handle.net/10230/33180
dc.description Treball fi de màster de: Master in Computational Biomedical Engineering
dc.description.abstract In 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Fetus -- Malalties
dc.subject.other Diagnòstic prenatal
dc.title Ultrasound segmentation for vascular network reconstruction in twin-to-twin transfusion syndrome
dc.type info:eu-repo/semantics/masterThesis
dc.subject.keyword Placenta segmentation
dc.subject.keyword Vessel segmentation
dc.subject.keyword Random walker algorithm
dc.subject.keyword GPU optimization
dc.subject.keyword Medical application
dc.subject.keyword Tutors: Miguel Ángel González Ballester i Mario Ceresa
dc.rights.accessRights info:eu-repo/semantics/openAccess


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