Needle Localization for Robot-assisted Subretinal Injection based on Deep Learning

Subretinal injection is known to be a complicated task for ophthalmologists to perform, the main sources of difficulties are the fine anatomy of the retina, insufficient visual feedback, and high surgical precision. Image guided robot-assisted surgery is one of the promising solutions that bring significant surgical enhancement in treatment outcome and reduces the physical limitations of human surgeons. In this paper, we demonstrate a robust framework for needle detection and localization in subretinal injection using microscope-integrated Optical Coherence Tomography (MI-OCT) based on deep learning. The proposed method consists of two main steps: a) the preprocessing of OCT volumetric images; b) needle localization in the processed images. The first step is to coarsely localize the needle position based on the needle information above the retinal surface and crop the original image into a small region of interest (ROI). Afterward, the cropped small image is fed into a well trained network for detection and localization of the needle segment. The entire framework is extensively validated in ex-vivo pig eye experiments with robotic subretinal injection. The results show that the proposed method can localize the needle accurately with a confidence of 99.2%.