# Spinal nerve segmentation method and dataset construction in endoscopic surgical scenarios

Source: [Spinal nerve segmentation method and dataset construction in endoscopic surgical scenarios](<https://www.sota2.com/research/paper/68fa70181e209131cea48f05>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-22T10:01:48.415Z

Authors: Shaowu Peng, Pengcheng Zhao, Yongyu Ye, Junying Chen, Yunbing Chang, Xiaoqing Zheng

Published: 2023-07-20T15:26:57.000Z

## 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 .

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.10955>)

[PDF](<https://arxiv.org/pdf/2307.10955.pdf>)