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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 Yuan1,3, 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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