The Retinal Vascular Tree (RVT) segmentation is required to diagnose various ocular pathologies. Recently, fundus images are acquired with higher resolution, which allows representing a large range of vessel thickness. However, standard Deep Learning (DL) architectures with static and small convolution size have failed to achieve higher segmentation performance. In this paper, we propose a novel DL architecture for RVT segmentation dedicated for high resolution fundus images. The idea consists at extending the U-net architecture by increasing (e.g. decreasing) convolution kernel size through convolution blocs, in correlation with downscale (e.g. upscale) of feature map dimensions. The proposed architecture is validated on HRF database, where average sensitivity is increased from 56% to 84%.