3D Intra-articular Dense Reconstruction from Arthroscopic Images

Yanhao Zhang, Chonghao Cheng, Raphael Falque, Liang Zhao, Shoudong Huang, Yongbo Chen

Abstract: Arthroscopy is an important clinical technique for knee joint damage treatment, where a small fibre-optic camera is manipulated in a narrow space to provide a live 2D view of the inner joint. The small field of view from the surgical scope makes the 3D guidance for instruments manipulation very difficult. Therefore, a 3D reconstruction of the articular surface becomes very helpful. This paper provides a framework that recovers joint inner dense surface for arthroscopy using only a sequence of intra-articular images. A self-supervised convolutional neural network is trained pre-operatively to predict dense depth maps from the intra-articular images. The depth maps and images are fused together and a local-to-global SLAM pipeline is proposed for tissue surface reconstruction. Compared with existing works on 3D reconstruction using arthroscopic images, the proposed framework does not require additional sensors. Ex-vivo experiments are conducted and the result demonstrates reconstruction ability as well as the potential clinical value of this framework.