  A large number of retinal vessel analysis methods based on image segmentation
have emerged in recent years. However, existing methods depend on cumbersome
backbones, such as VGG16 and ResNet-50, benefiting from their powerful feature
extraction capabilities but suffering from high computational costs. In this
paper, we propose a novel neural network (HybridNetSeg) dedicated to solving
this drawback while further improving overall performance. Considering
deformable convolution can extract complex and variable structural information,
and larger kernel in mixed depthwise convolution makes contribution to higher
accuracy. We have integrated these two modules and propose a Hybrid Convolution
Block (HCB) using the idea of heuristic learning. Inspired by the U-Net, we use
HCB to replace a part of the common convolution of the U-Net encoder,
drastically reducing the parameter count to 0.71M while accelerating the
inference process. Not only that, we also propose a multi-scale mixed loss
mechanism. Extensive experiments on three major benchmark datasets demonstrate
the effectiveness of our proposed method
