Semantic-Embedding and Shape-Aware U-Net for Ultrasound Eyeball Segmentation

Abstract

Segmentation of eyeball region from ultrasound images is a new research direction for the diagnosis of ophthalmic diseases. Despite the advantages of convenience and cheapness, ultrasound images bring more noise and fuzzy contour compared with other medical images. Existing methods fail to give a segmentation with reasonable eyeball shape, especially when the contour is ambiguous. In this paper, we propose a novel framework based on convolutional neural network, named semantic-embedding and shape-aware U-Net, to deal with the segmentation in blurred images. A signed distance field is used as label instead of the traditional binary mask label to add shape prior in network. The applying of semantic embedding modules fuses semantic information between different stages of the network. Experimental results show that our method improves the ability to segment image with blurred edges and outperforms existing methods in the accuracy of segmentation.