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Conference papers
Fully automatic CNN-based segmentation of retinal bifurcations in 2D adaptive optics ophthalmoscopy images
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.
Cited literature [23 references]
https://hal.archives-ouvertes.fr/hal-02925043
Contributor : Florence Rossant <>
Submitted on : Friday, August 28, 2020 - 4:28:35 PM
Last modification on : Saturday, October 10, 2020 - 1:51:00 AM
Contributor : Florence Rossant <>
Submitted on : Friday, August 28, 2020 - 4:28:35 PM
Last modification on : Saturday, October 10, 2020 - 1:51:00 AM
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IPTA_2020.pdf
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