NerveStitcher: Corneal confocal microscope images stitching with neural networks

Abstract

Corneal nerves are of great interest to clinicians and scientists due to their potential for the diagnosis of early neurological disorders. In vivo confocal microscopy (IVCM) has been used as a novel and reliable tool for observing and quantifying corneal sub-basal nerves. Creating a wide-field montage of the nerve plexus from a large amount of IVCM images facilitates the measurement of corneal nerve morphology. In this paper, we propose a fully automatic image stitching method using neural networks. Firstly, we extend a self-supervised point detector to find the feature points on IVCM images. Then a flexible points correspondence based on the attention mechanism is developed for partial assignment of image pair. The scattered IVCM images are consequently integrated and fused according to the local offsets. We experimented with our method on 30 sets of IVCM images. Compared to conventional methods, our method improves matching accuracy and significantly reduces processing time. And by calculating the morphological parameters of the corneal nerve for both single images and stitched images, our method can evaluate the corneal nerve of patients more accurately and reliably. The implemented code is available at https://github.com/LiTianYu6/NerveStitcher.