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Fully automatic CNN-based segmentation of retinal bifurcations in 2D adaptive optics ophthalmoscopy images

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Abstract

Automated image segmentation is a crucial step to characterize and quantify the morphometry of blood vessels. Adaptive Optics Ophthalmoscopy (AOO) images of eye fundus allow visualizing retinal vessels with a high resolution, especially arterial bifurcations, suitable to morphometric biomarkers measurements. In this paper, we propose a fully automatic hybrid approach based on a modified U-Net convolutional neu-ral network and active contours for segmenting retinal vessel branches and bifurcations with high precision. The obtained segmentation results are within the range of intra-and inter-user variability, and meet the performance of our previous semi-automatic approach in terms of precision and reproducibility, while being obtained in a completely automatic way.
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Dates and versions

hal-02925043 , version 1 (28-08-2020)

Identifiers

  • HAL Id : hal-02925043 , version 1

Cite

Iyed Trimeche, Florence Rossant, Isabelle Bloch, Michel Pâques. Fully automatic CNN-based segmentation of retinal bifurcations in 2D adaptive optics ophthalmoscopy images. International Conference on Image Processing Theory, Tools and Applications (IPTA 2020), Nov 2020, PARIS, France. ⟨hal-02925043⟩
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