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: this https URL .

本文介绍了一种用于脊柱手术的首个实时分割方法，通过视频引导避免对脊神经的损伤，为外科医生提供重要的导航信息。此方法基于一份约 10,000 帧的细致注释分割数据集，针对语义分割问题，提出了 FUnet (Frame-Unet) 模型，通过利用帧间信息和自我注意机制，实现了业界最先进的性能。我们还对相似的息肉内窥镜视频数据集进行了拓展实验，结果表明该模型具有良好的泛化能力和优越的性能。本文提供了数据集和代码，详见此 https URL。

腔镜手术场景下的脊神经分割方法和数据集构建

Spinal nerve segmentation method and dataset construction in endoscopic  surgical scenarios

Video segmentation for the human head and shoulders is essential in creating
elegant media for videoconferencing and virtual reality applications. The main
challenge is to process high-quality background subtraction in a real-time
manner and address the segmentation issues under motion blurs, e.g., shaking
the head or waving hands during conference video. To overcome the motion blur
problem in video segmentation, we propose a novel flow-based encoder-decoder
network (FUNet) that combines both traditional Horn-Schunck optical-flow
estimation technique and convolutional neural networks to perform robust
real-time video segmentation. We also introduce a video and image segmentation
dataset: ConferenceVideoSegmentationDataset. Code and pre-trained models are
available on our GitHub repository:
https://github.com/kuangzijian/Flow-Based-Video-Matting.

本文提出了一种基于流的编码器解码器网络（FUNet）来处理高质量的背景减除和克服图像中的运动模糊问题，以解决视频分割在视频会议和虚拟现实应用中的问题，并且介绍了一个名为 ConferenceVideoSegmentationDataset 的视频和图像分割数据集。