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Fully-automatic segmentation of the ciliary muscle using anterior segment optical coherence tomography images
dc.contributor.author | Goyanes, Elena | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Fernández-Vigo, José Ignacio | |
dc.contributor.author | Fernández-Vigo, José A. | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-05-06T18:14:13Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | E. Goyanes, J. de Moura, J. Novo, J. I. Fernández-Vigo, J. Á. Fernández-Vigo and M. Ortega, "Fully-automatic segmentation of the ciliary muscle using anterior segment optical coherence tomography images," 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1-8, doi: 10.1109/IJCNN55064.2022.9892316. | es_ES |
dc.identifier.isbn | 978-1-7281-8671-9 | |
dc.identifier.issn | 2161-4407 | |
dc.identifier.uri | http://hdl.handle.net/2183/36419 | |
dc.description | This version of the paper has been accepted for publication. The final published paper is available online at: https://doi.org/10.1109/IJCNN55064.2022.9892316 | es_ES |
dc.description.abstract | [Abstract]: The study of the ciliary muscle represents a fundamental step in the diagnosis and treatment of many high-incidence diseases, such as glaucoma or myopia. Currently, Anterior Segment Optical Coherence Tomography (AS-OCT) is widely used by clinicians to analyse the morphological changes that affect this important ocular structure. AS-OCT is a non-invasive imaging technique that produces high-resolution cross-sectional images, allowing a precise visualization of the main ocular tissues of the anterior segment of the eye. In this work, we propose a novel methodology for the ciliary muscle segmentation using AS-OCT images, an emerging ophthalmic imaging technology with great potential to support early diagnosis of relevant ocular conditions. For this purpose, we have analysed the performance of the U-Net architecture with two different encoders (ResNet-18 and ResNet-34) combined with a transfer learning-based approach. The validation of the proposed system was performed through different and representative experiments, using an AS-OCT dataset that was specifically designed for this work. The results demonstrated that the proposed system is robust and reliable, achieving an average Precision of 0.8902 ± 0.0815, an average Recall of 0.8237 ± 0.1239, an average Accuracy of 0.9961 ± 0.0021, an average Jaccard of 0.7431 ± 0.1116 and an average Dice of 0.8445 ± 0.0870. These results demonstrate that the proposed method has a satisfactory performance that can help the clinicians to make a more accurate diagnosis and proceed with appropriate treatments of different diseases of interest. | es_ES |
dc.description.sponsorship | This research was funded by ISCIII, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; CCEU, Xunta de Galicia through the postdoctoral grant contract ref. ED481B 2021/059; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from CCEU, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481B 2021/059 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/24 | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN845D 2020/38 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS18%2F00136/ES/Plataforma online para prevención y detección precoz de enfermedad vascular mediante análisis automatizado de información e imagen clínica | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095894-B-I00/ES/DESARROLLO DE TECNOLOGIAS INTELIGENTES PARA DIAGNOSTICO DE LA DMAE BASADAS EN EL ANALISIS AUTOMATICO DE NUEVAS MODALIDADES HETEROGENEAS DE ADQUISICION DE IMAGEN OFTALMOLOGICA | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACION Y CARACTERIZACION COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLOGICA: ESTUDIOS EN ESCLEROSIS MULTIPLE | es_ES |
dc.relation.uri | https://doi.org/10.1109/IJCNN55064.2022.9892316 | es_ES |
dc.rights | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | es_ES |
dc.subject | CAD system | es_ES |
dc.subject | AS-OCT | es_ES |
dc.subject | Ciliary muscle | es_ES |
dc.subject | Segmentation | es_ES |
dc.subject | Deep Learning | es_ES |
dc.title | Fully-automatic segmentation of the ciliary muscle using anterior segment optical coherence tomography images | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.date.embargoEndDate | 2024-09-30 | es_ES |
dc.date.embargoLift | 2024-09-30 | |
UDC.volume | 2022 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 8 | es_ES |
dc.identifier.doi | 10.1109/IJCNN55064.2022.9892316 | |
UDC.conferenceTitle | International Joint Conference on Neural Networks (IJCNN) | es_ES |