用YOLOv8-Pose实现腹腔镜手术实时3D可视化，提升精度与效率
EasyVis2: A Real Time Multi-view 3D Visualization System for Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose
- 通过多摄像头+YOLOv8-Pose估计器械2D姿态，融合计算3D位姿
- 相同摄像头数下3D重建精度提升，计算时间更短，2D定位准确率高
- 适合手术训练系统开发者、医学影像算法研究者使用
EasyVis2 是一个为腹腔镜手术训练设计的无手操作、实时三维可视化系统。该系统采用内置微型摄像头阵列的手术套管，可插入体腔内，提供增强视野和手术过程的三维视角。利用专用深度神经网络算法 YOLOv8-Pose，系统在每个相机视图中估计手术器械的位置与朝向。基于多视角的估计结果，计算出器械的三维姿态，并渲染出叠加于背景场景上的三维器械模型，实现实时可视化。本研究提出将 YOLOv8-Pose 适配至 EasyVis2 系统的方法，包括构建定制化训练数据集。实验表明，在相同摄像头数量下，新系统提升了三维重建精度并降低了计算时间；同时，适配后的 YOLOv8-Pose 在二维姿态估计上表现出高精度。
原文摘要 · Abstract (English)
EasyVis2 is a system designed to provide hands-free, real-time 3D visualization for laparoscopic surgery. It incorporates a surgical trocar equipped with an array of micro-cameras, which can be inserted into the body cavity to offer an enhanced field of view and a 3D perspective of the surgical procedure. A specialized deep neural network algorithm, YOLOv8-Pose, is utilized to estimate the position and orientation of surgical instruments in each individual camera view. These multi-view estimates enable the calculation of 3D poses of surgical tools, facilitating the rendering of a 3D surface model of the instruments, overlaid on the background scene, for real-time visualization. This study presents methods for adapting YOLOv8-Pose to the EasyVis2 system, including the development of a tailored training dataset. Experimental results demonstrate that, with an identical number of cameras, the new system improves 3D reconstruction accuracy and reduces computation time. Additionally, the adapted YOLOv8-Pose system shows high accuracy in 2D pose estimation.
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