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• W3114495296 abstract "The identification of nerve is difficult as structures of nerves are challenging to image and to detect in ultrasound images. Nevertheless, the nerve identification in ultrasound images is a crucial step to improve performance of regional anesthesia. In this paper, a network called Brachial Plexus Multi-instance Segmentation Network (BPMSegNet) is proposed to identify different tissues (nerves, arteries, veins, muscles) in ultrasound images. The BPMSegNet has three novel modules. The first is the spatial local contrast feature, which computes contrast features at different scales. The second one is the self-attention gate, which reweighs the channels in feature maps by their importance. The third is the addition of a skip concatenation with transposed convolution within a feature pyramid network. The proposed BPMSegNet is evaluated by conducting experiments on our constructed Ultrasound Brachial Plexus Dataset (UBPD). Quantitative experimental results show the proposed network can segment multiple tissues from the ultrasound images with a good performance." @default.
• W3114495296 created "2021-01-05" @default.
• W3114495296 creator A5030009218 @default.
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• W3114495296 date "2020-12-22" @default.
• W3114495296 modified "2023-09-23" @default.
• W3114495296 title "Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet." @default.
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• W3114495296 hasPublicationYear "2020" @default.
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