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Unsupervised Deformable Image Registration in a Landmark Scarcity Scenario: Choroid OCTA
dc.contributor.author | López-Varela, Emilio | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Fernández-Vigo, José Ignacio | |
dc.contributor.author | Moreno-Morillo, Francisco Javier | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-05-21T18:33:07Z | |
dc.date.available | 2024-05-21T18:33:07Z | |
dc.date.issued | 2022-05 | |
dc.identifier.citation | López-Varela, E., Novo, J., Fernández-Vigo, J.I., Moreno-Morillo, F.J., Ortega, M. (2022). Unsupervised Deformable Image Registration in a Landmark Scarcity Scenario: Choroid OCTA. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_8 | es_ES |
dc.identifier.isbn | 978-3-031-06426-5 | |
dc.identifier.isbn | 978-3-031-06427-2 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/2183/36565 | |
dc.description | This version of the conference paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-06427-2_8. | es_ES |
dc.description.abstract | [Abstract]: Recent advances in OCTA allow the imaging of blood flow deeper than the retinal layers at the level of the choriocapillaris (CC), where a pattern of small dark areas represents the absence of flow, called flow voids. The distribution of flow voids can be used as a biomarker to diagnose and monitor the progression of relevant pathologies or the efficacy of applied treatments. A pixel-to-pixel comparison can help to carry out this monitoring effectively, although in order to carry out this comparison, the used images must be perfectly aligned. CC images are characterized by their granularity, presenting numerous and complex local deformations, so a deformable registration is necessary to carry out a reliable comparison. However, CC OCTA images also present a characteristic absence of visually significant anatomical structures. This landmark scarcity hardens drastically the identification of points of interest to achieve an accurate registration. Based on this context, we designed a methodology to accurately perform this deformable registration in this challenging scenario. Hence, we propose a convolutional neural network model trained by unsupervised learning to register images in a real clinical scenario, being obtained at different time instants from patients with central serous chorioretinopathy (CSC) treated with photodynamic therapy. Our methodology produces superior alignment to those achieved with other proven methods, helping to improve the monitoring of the efficacy of photodynamic therapy applied to patients with CSC. Our robust and adaptable methodology can also be exploited in other similar scenarios of complex registrations with anatomical landmark scarcity. | es_ES |
dc.description.sponsorship | This research was funded by Instituto de Salud Carlos III, 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; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, 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 Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. | 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 | Springer | 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/10.1007/978-3-031-06427-2_8 | es_ES |
dc.rights | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG | es_ES |
dc.subject | Ophthalmology | es_ES |
dc.subject | OCTA imaging | es_ES |
dc.subject | Choriocapillaris | es_ES |
dc.subject | Deformable image registration | es_ES |
dc.subject | Flow voids | es_ES |
dc.subject | Convolutional Neural Networks | es_ES |
dc.title | Unsupervised Deformable Image Registration in a Landmark Scarcity Scenario: Choroid OCTA | 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/openAccess | es_ES |
UDC.volume | Lecture Notes in Computer Science, vol 13231 | es_ES |
UDC.startPage | 89 | es_ES |
UDC.endPage | 99 | es_ES |
dc.identifier.doi | 10.1007/978-3-031-06427-2_8 | |
UDC.conferenceTitle | International Conference on Image Analysis and Processing – ICIAP 2022. | es_ES |