Spinal nerve segmentation method and dataset construction in endoscopic surgical scenarios

TL;DR

This paper introduces a real-time spinal nerve segmentation method for endoscopic surgery, supported by a newly constructed annotated dataset, and demonstrates state-of-the-art performance with good generalization.

Contribution

It presents the first real-time segmentation approach for spinal nerves in endoscopic surgery and provides a large annotated dataset for this purpose.

Findings

Achieves state-of-the-art segmentation performance.

Demonstrates good generalization on related endoscopy datasets.

Provides a valuable dataset and code for future research.

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 .