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Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images

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Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images

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dc.contributor.author del Amor, Rocío es_ES
dc.contributor.author Morales, Sandra es_ES
dc.contributor.author Colomer, Adrián es_ES
dc.contributor.author Mossi García, José Manuel es_ES
dc.contributor.author Woldbye, David es_ES
dc.contributor.author Klemp, Kristian es_ES
dc.contributor.author Larsen, Michael es_ES
dc.contributor.author Naranjo Ornedo, Valeriana es_ES
dc.date.accessioned 2019-09-17T12:32:43Z
dc.date.available 2019-09-17T12:32:43Z
dc.date.issued 2019
dc.identifier.isbn 978-9-0827-9702-2
dc.identifier.issn 2076-1465
dc.identifier.uri http://hdl.handle.net/10251/125893
dc.description.abstract [EN] Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52±0.80 µm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 µm. es_ES
dc.description.sponsorship This work was supported by the Project GALAHAD [H2020-ICT-2016-2017, 732613]. es_ES
dc.format.extent 5 es_ES
dc.language Inglés es_ES
dc.publisher IEEE es_ES
dc.relation.ispartof 2019 27th European Signal Processing Conference (EUSIPCO) es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Optical coherence tomography es_ES
dc.subject Rodent OCT es_ES
dc.subject Layer segmentation es_ES
dc.subject Convolutional neural networks es_ES
dc.subject Glaucoma assessment es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/732613/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.description.bibliographicCitation Del Amor, R.; Morales, S.; Colomer, A.; Mossi García, JM.; Woldbye, D.; Klemp, K.; Larsen, M.... (2019). Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images. En 2019 27th European Signal Processing Conference (EUSIPCO). IEEE. http://hdl.handle.net/10251/125893 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename European Signal Processing Conference (EUSIPCO) (formerly European Signal and Image Processing Conference) es_ES
dc.relation.conferencedate Septiembre 02-07,2019 es_ES
dc.relation.conferenceplace A Coruña, España es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\393233 es_ES
dc.contributor.funder European Commission es_ES


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