Spinal nerve segmentation method and dataset construction in endoscopic
  surgical scenarios
Shaowu Peng Pengcheng Zhao Yongyu Ye Junying Chen Yunbing Chang Xiaoqing Zheng
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 .