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dc.contributor.author | García-Pardo, José Gabriel | es_ES |
dc.contributor.author | Colomer, Adrián | es_ES |
dc.contributor.author | Naranjo Ornedo, Valeriana | es_ES |
dc.date.accessioned | 2021-04-17T03:32:51Z | |
dc.date.available | 2021-04-17T03:32:51Z | |
dc.date.issued | 2021-03 | es_ES |
dc.identifier.issn | 0169-2607 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/165287 | |
dc.description.abstract | [EN] Background and objective:Glaucoma is the leading cause of blindness worldwide. Many studies based on fundus image and optical coherence tomography (OCT) imaging have been developed in the literature to help ophthalmologists through artificial-intelligence techniques. Currently, 3D spectral-domain optical coherence tomography (SD-OCT) samples have become more important since they could enclose promising information for glaucoma detection. To analyse the hidden knowledge of the 3D scans for glaucoma detection, we have proposed, for the first time, a deep-learning methodology based on leveraging the spatial dependencies of the features extracted from the B-scans. Methods:The experiments were performed on a database composed of 176 healthy and 144 glaucomatous SD-OCT volumes centred on the optic nerve head (ONH). The proposed methodology consists of two well-differentiated training stages: a slide-level feature extractor and a volume-based predictive model. The slide-level discriminator is characterised by two new, residual and attention, convolutional modules which are combined via skip-connections with other fine-tuned architectures. Regarding the second stage, we first carried out a data-volume conditioning before extracting the features from the slides of the SD-OCT volumes. Then, Long Short-Term Memory (LSTM) networks were used to combine the recurrent dependencies embedded in the latent space to provide a holistic feature vector, which was generated by the proposed sequential-weighting module (SWM). Results:The feature extractor reports AUC values higher than 0.93 both in the primary and external test sets. Otherwise, the proposed end-to-end system based on a combination of CNN and LSTM networks achieves an AUC of 0.8847 in the prediction stage, which outperforms other state-of-the-art approaches intended for glaucoma detection. Additionally, Class Activation Maps (CAMs) were computed to highlight the most interesting regions per B-scan when discerning between healthy and glaucomatous eyes from raw SD-OCT volumes. Conclusions:The proposed model is able to extract the features from the B-scans of the volumes and combine the information of the latent space to perform a volume-level glaucoma prediction. Our model, which combines residual and attention blocks with a sequential weighting module to refine the LSTM outputs, surpass the results achieved from current state-of-the-art methods focused on 3D deep-learning architectures. | es_ES |
dc.description.sponsorship | The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used here. | es_ES |
dc.description.sponsorship | This work has been funded by GALAHAD project [H2020-ICT-2016-2017, 732613], SICAP project (DPI2016-77869-C2-1-R) and GVA through project PROMETEO/2019/109. The work of Gabriel García has been supported by the State Research Spanish Agency PTA2017-14610-I. | |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Computer Methods and Programs in Biomedicine | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Glaucoma detection | es_ES |
dc.subject | SD-OCT volumes | es_ES |
dc.subject | Convolutional attention blocks | es_ES |
dc.subject | Residual connections | es_ES |
dc.subject | LSTM networks | es_ES |
dc.subject | Sequential-weighting module | es_ES |
dc.subject.classification | TEORIA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.cmpb.2020.105855 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PTA2017-14610-I/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/732613/EU/GLAUCOMA - ADVANCED, LABEL-FREE HIGH RESOLUTION AUTOMATED OCT DIAGNOSTICS/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/GV//PROMETEO/2019/109/ES/COMUNICACION Y COMPUTACION INTELIGENTES Y SOCIALES/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//DPI2016-77869-C2-1-R/ES/SISTEMA DE INTERPRETACION DE IMAGENES HISTOPATOLOGICAS PARA LA DETECCION DE CANCER DE PROSTATA/ | |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions | es_ES |
dc.description.bibliographicCitation | García-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V. (2021). Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies. Computer Methods and Programs in Biomedicine. 200:1-16. https://doi.org/10.1016/j.cmpb.2020.105855 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.cmpb.2020.105855 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 16 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 200 | es_ES |
dc.identifier.pmid | 33303289 | es_ES |
dc.relation.pasarela | S\422846 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Generalitat Valenciana | |
dc.contributor.funder | European Commission | |
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