Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images
Diabetes represents one of the main causes of blindness in developed countries, caused by fluid accumulations in the retinal layers. The clinical literature defines the different types of diabetic macular edema (DME) as cystoid macular edema (CME), diffuse retinal thickening (DRT), and serous retina...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Servizo Galego de Saúde (SERGAS) |
| Repositorio: | RUNA. Repositorio da Consellería de Sanidade e Sergas |
| OAI Identifier: | oai:runa.sergas.gal:20.500.11940/21764 |
| Acesso em linha: | https://portalcientifico.sergas.gal//documentos/63df0a616fdec82c4e7de7bd http://hdl.handle.net/20.500.11940/21764 |
| Access Level: | acceso abierto |
| Palavra-chave: | Humans Macular Edema Diabetic Retinopathy Uncertainty Tomography, Optical Coherence Visual Acuity Retrospective Studies AS A Coruña INIBIC |
| Resumo: | Diabetes represents one of the main causes of blindness in developed countries, caused by fluid accumulations in the retinal layers. The clinical literature defines the different types of diabetic macular edema (DME) as cystoid macular edema (CME), diffuse retinal thickening (DRT), and serous retinal detachment (SRD), each with its own clinical relevance. These fluid accumulations do not present defined borders that facilitate segmentational approaches (specially the DRT type, usually not taken into account by the state of the art for this reason) so a diffuse paradigm is used for its detection and visualization. In this paper, we propose three novel approaches for the representation and characterization of these types of DME. A baseline proposal, using a convolutional neural network as backbone, another based on transfer learning from a general domain, and a third approach exploiting information of regions without a defined label. Overall, our baseline proposal obtained an AUC of 0.9583 ± 0.0093, the approach pretrained with a general-domain dataset an AUC of 0.9603 ± 0.0087, and the approach pretrained in the domain taking advantage of uncertainty, an AUC of 0.9619 ± 0.0073. [Figure not available: see fulltext.]. |
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