Real-Time Tracking of Corneal Contour in Dalk Surgical Navigation Using Deep Neural Networks

Corneal disease is one of the most common causes of blindness for human beings in the world. Deep anterior lamellar k-eratoplasty (DALK) is a widely-used corneal transplantation technique, which requires precise control of surgical tools. This paper proposes a deep learning framework of augmented reality (AR) based surgical navigation to guide the suturing process in DALK. It aims to track the cutting corneal contour robustly through semantic segmentation and occlusion reconstruction. We devise a novel optical flow inpainting network to restore the missing motion caused by occlusion. The occluded regions are obtained using weakly-supervised segmentation of surgical tools and reconstructed by the key-frame warping along the completed optical flow. We introduce two kinds of loss functions to adapt the inpainting network to the optical flow space. The performance of our techniques is evaluated using real surgery videos from Shandong Eye Hospital. All experimental results show that our approach can achieve accurate corneal contour tracking subject to complex disturbance of tools in real-time surgical scenarios.