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The morphological dynamics of the median nerve across the level extracted from dynamic ultrasonography are valuable for the diagnosis and evaluation of carpal tunnel syndrome (CTS), but the data extraction requires tremendous labor to manually segment the nerve across the image sequence. Our aim was to provide visually real-time, automated median nerve segmentation and subsequent data extraction in dynamic ultrasonography. We proposed a deep-learning model modified from SOLOv2 and tailored fordoi:10.1016/j.ultrasmedbio.2022.12.014 pmid:36740461 fatcat:67xiklvbefhk7pzedo3evregui