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Low-Resolution Retinal Image Vessel Segmentation
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| Segmentation process serves to aid the pathology diagnosing process since segmentation filters the interference from other anatomical structures and helps focus on the posterior segment structures of the eye, highlighting a set of signals that will serve for diagnosis of various retinal pathologies. Automatic retinal vessel segmentation can lead to a more accurate diagnosis. This paper presents a framework for automatic vessel segmentation of lower-resolution retinal images taken with a smartphone equipped with D-EYE lens. The framework is evaluated and the attained results were presented. A dataset was assembled and annotated of train models for automatic localisation retinal areas and for vessel segmentation. For the framework, two CNN based models were successfully trained, a Faster R-CNN that achieved a 96% correct detected of all regions with an MAE of 39 pixels, and a U-Net that achieved a DICE of 0.7547. | 3.56 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Segmentation process serves to aid the pathology diagnosing process since segmentation filters the interference from other anatomical structures and helps focus on the posterior segment structures of the eye, highlighting a set of signals that will serve for diagnosis of various retinal pathologies. Automatic retinal vessel segmentation can lead to a more accurate diagnosis. This paper presents a framework for automatic vessel segmentation of lower-resolution retinal images taken with a smartphone equipped with D-EYE lens. The framework is evaluated and the attained results were presented. A dataset was assembled and annotated of train models for automatic localisation retinal areas and for vessel segmentation. For the framework, two CNN based models were successfully trained, a Faster R-CNN that achieved a 96% correct detected of all regions with an MAE of 39 pixels, and a U-Net that achieved a DICE of 0.7547.
Descrição
EISBN - 9783030491086
Conference name - 14th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020; Conference date - 19 July 2020 - 24 July 2020; Conference code - 242149
Conference name - 14th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020; Conference date - 19 July 2020 - 24 July 2020; Conference code - 242149
Palavras-chave
Faster R-CNN U-Net Low-resolution retinal images Segmentation Screening
Contexto Educativo
Citação
Zengin, H., Camara, J., Coelho, P., Rodrigues, J.M.F., Cunha, A. (2020). Low-Resolution Retinal Image Vessel Segmentation. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Applications and Practice. HCII 2020. Lecture Notes in Computer Science(), vol 12189. Springer, Cham. https://doi.org/10.1007/978-3-030-49108-6_44.
Editora
Springer Nature
Licença CC
Sem licença CC