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arXiv · 2307.10955

Spinal nerve segmentation method and dataset construction in endoscopic surgical scenarios

Shaowu Peng·Pengcheng Zhao·Yongyu Ye·Junying Chen·Yunbing Chang·Xiaoqing Zheng
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Abstract

Endoscopic surgery is currently an important treatment method in the field of spinal surgery and avoiding damage to the spinal nerves through video guidance is a key challenge. This paper presents the first real-time segmentation method for spinal nerves in endoscopic surgery, which provides crucial navigational information for surgeons. A finely annotated segmentation dataset of approximately 10,000 consec-utive frames recorded during surgery is constructed for the first time for this field, addressing the problem of semantic segmentation. Based on this dataset, we propose FUnet (Frame-Unet), which achieves state-of-the-art performance by utilizing inter-frame information and self-attention mechanisms. We also conduct extended exper-iments on a similar polyp endoscopy video dataset and show that the model has good generalization ability with advantageous performance. The dataset and code of this work are presented at: https://github.com/zzzzzzpc/FUnet .

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Shaowu Peng, Pengcheng Zhao, Yongyu Ye, Junying Chen, Yunbing Chang, Xiaoqing Zheng. 2023-07-20. Spinal nerve segmentation method and dataset construction in endoscopic surgical scenarios. https://arxiv.org/abs/2307.10955

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