  Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may
cause blindness if the patient's condition is severe. Clinical results show
that SWS can be divided into two types based on the characteristics of scleral
blood vessels. Therefore, how to accurately segment scleral blood vessels has
become a significant problem in computer-aided diagnosis. In this research, we
propose to continuously upsample the bottom layer's feature maps to preserve
image details, and design a novel Claw UNet based on UNet for scleral blood
vessel segmentation. Specifically, the residual structure is used to increase
the number of network layers in the feature extraction stage to learn deeper
features. In the decoding stage, by fusing the features of the encoding,
upsampling, and decoding parts, Claw UNet can achieve effective segmentation in
the fine-grained regions of scleral blood vessels. To effectively extract small
blood vessels, we use the attention mechanism to calculate the attention
coefficient of each position in images. Claw UNet outperforms other UNet-based
networks on scleral blood vessel image dataset.
