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Abstract #2638

Comparison of Traditional fSNAP and 3D FuseUnet Based fSNAP

Chuyu Liu1, Shuo Chen1, and Rui Li1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China

By adapting 3D FuseUnet, CNN fSNAP showed better performance in lumen and IPH depiction compared with traditional fSNAP. The results suggest that deep learning can help fast SNAP scans produce high quality images, which could have great clinical utility.

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