内窥镜手术场景下的脊神经分割方法与数据集构建
Spinal nerve segmentation method and dataset construction in endoscopic surgical scenarios
- School of Software Engineering, South China University of Technology（华南理工大学软件学院）
- Guangdong Provincial People's Hospital（广东省人民医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对内窥镜脊柱手术中需避免损伤脊神经的挑战，构建了万级帧精细标注脊神经分割数据集，提出融合帧间信息与自注意力的FUnet模型，实现实时最优分割且泛化性良好。
AI中文摘要：
内窥镜手术是目前脊柱外科领域的重要治疗手段，通过视频引导避免损伤脊神经是一项关键挑战。本文提出了首个内窥镜手术中脊神经的实时分割方法，可为外科医生提供关键的导航信息。本文首次在该领域构建了包含约10000帧手术过程中连续录制帧的精细标注分割数据集，解决了语义分割的数据支撑问题。基于该数据集，我们提出FUnet（Frame-Unet），该模型利用帧间信息和自注意力机制取得了当前最优性能。我们还在类似的息肉内窥镜视频数据集上开展了扩展实验，结果表明该模型具备良好的泛化能力，性能表现优异。本工作的数据集和代码可在以下地址获取：https://github.com/zzzzzzpc/FUnet 。
英文摘要：
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 .
