Abstract #4743

Automatic Segmentation of Carotid Vessel Wall on GOAL-SNAP Images using SE-UNet

Yuze Li¹, Haikun Qi², Huiyu Qiao¹, Hualu Han¹, Xihai Zhao¹, Chun Yuan^1,3, and Huijun Chen¹

¹Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, ²School of Biomedical Engineering and Imaging Sciences, King's College, London, United Kingdom, ³Department of Radiology, University of Washington, Seattle, WA, United States

In this work, we proposed a deep learning structure called SE-UNet for carotid vessel wall segmentation on 3D golden angle radial k-space sampling simultaneous non-contrast angiography and intraplaque hemorrhage (GOAL-SNAP) images. The structure of network consisted of an encoder path for feature extraction and a decoder path for precise localization. The squeeze-and-excitation (SE) module was introduced to the encoder part to learn the context between channels. The proposed SE-UNet achieved high IOU of 0.786, and high pixel-wise sensitivity of 0.976, specificity of 0.850.

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