Deep Multi-Segmentation Approach for the Joint Classification and Segmentation of the Retinal Arterial and Venous Trees in Color Fundus Images
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http://hdl.handle.net/2183/29453
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Deep Multi-Segmentation Approach for the Joint Classification and Segmentation of the Retinal Arterial and Venous Trees in Color Fundus ImagesData
2021Cita bibliográfica
Morano, J.; Hervella, Á.S.; Novo, J.; Rouco, J. Deep Multi-Segmentation Approach for the Joint Classification and Segmentation of the Retinal Arterial and Venous Trees in Color Fundus Images. Eng. Proc. 2021, 7, 22. https://doi.org/10.3390/engproc2021007022
Resumo
[Abstract] The analysis of the retinal vasculature represents a crucial stage in the diagnosis of several diseases. An exhaustive analysis involves segmenting the retinal vessels and classifying them into veins and arteries. In this work, we present an accurate approach, based on deep neural networks, for the joint segmentation and classification of the retinal veins and arteries from color fundus images. The presented approach decomposes this joint task into three related subtasks: the segmentation of arteries, veins and the whole vascular tree. The experiments performed show that our method achieves competitive results in the discrimination of arteries and veins, while clearly enhancing the segmentation of the different structures. Moreover, unlike other approaches, our method allows for the straightforward detection of vessel crossings, and preserves the continuity of the arterial and venous vascular trees at these locations.
Palabras chave
Medical imaging
Vessel segmentation
Artery and vein classification
Deep learning
Vessel segmentation
Artery and vein classification
Deep learning
Descrición
Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.
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Atribución 4.0 Internacional