Design of algorithms for the construction of 3-dimensional models of the human crystalline lens

Ruiz Calvo, Andrés

Publication:
Design of algorithms for the construction of 3-dimensional models of the human crystalline lens

dc.contributor.advisor	Martínez Enríquez, Eduardo
dc.contributor.advisor	Díaz de María, Fernando
dc.contributor.author	Ruiz Calvo, Andrés
dc.contributor.departamento	UC3M. Departamento de Teoría de la Señal y Comunicaciones	es
dc.date.accessioned	2022-12-02T17:59:20Z
dc.date.available	2022-12-02T17:59:20Z
dc.date.issued	2022-06
dc.date.submitted	2022-07-12
dc.description.abstract	Quantification of the anterior segment of the human eye (cornea, iris, and crystalline lens) is crucial for the diagnosis and treatment of common eye conditions such as presbyopia and cataracts. Quantification requires measurement of patient’s eye and the construction of accurate 3-D models from these measurements. Measurements are usually obtained using a non-invasive high-resolution optical imaging technique known as Optical Coherence Tomography (OCT). From these measurements, one of the most relevant steps to obtain accurate 3-D models is the development of high-quality and reliable image segmentation algorithms. In this bachelor’s thesis we first describe state-of-the-art image segmentation algorithms of the anterior segment of the eye, which are based on traditional image processing techniques. Also, we present a discussion about their results and shortcomings. Then, we present the main goal of this project: to improve the performance and address the shortcomings of the traditional image processing-based segmentation algorithms. To do so, an image segmentation model based on deep learning was created. A comparison between both image segmentation models will be carried out. The results obtained show that model performance and reliability, key aspects for the quantification of anterior segment of the human eye, are superior using deep learning. The proposed deep learning model could have a strong positive impact on the quantification of the human eye, which in turn could help in the diagnosis and treatment of high-prevalence eye conditions.	es
dc.description.degree	Ciencia e Ingeniería de datos	es
dc.identifier.uri	https://hdl.handle.net/10016/36153
dc.language.iso	eng	es
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.accessRights	open access	es
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.eciencia	Telecomunicaciones	es
dc.subject.other	Image segmentation	es
dc.subject.other	Deep learning	es
dc.subject.other	Transfer learning	es
dc.subject.other	Optical Coherence Tomography (OCT)	es
dc.subject.other	Anterior segment of the eye	es
dc.title	Design of algorithms for the construction of 3-dimensional models of the human crystalline lens	es
dc.type	bachelor thesis	*
dspace.entity.type	Publication