Corneal confocal microscopy (CCM) is a new technique offering non-invasive and fast imaging useful for diagnosing and analyzing corneal diseases. The morphology of corneal nerve fibres can be clearly observed from CCM images. Segmentation and quantification of nerve fibres is important for analyzing corneal diseases such as diabetic peripheral neuropathy (DPN). In this paper, we propose an automated deep learning based method for corneal nerve fibre segmentation in CCM images. The main contributions of this paper are: (1)We add multi-scale split and concatenate (MSC) blocks to the decoding part of the four layer U-Net architecture. (2) A new loss function is applied that combining the Dice loss with the fibre length difference between the ground truth and the prediction. The method was tested on a dataset containing 90 CCM images from 4 normal eyes and 4 eyes with corneal diseases. The Dice coefficient of our approach can reach 87.96%, improves 1.6% compared with the baseline, and outperforms some existing deep networks for segmentation.

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131Z (10 March 2020); doi: 10.1117/12.2548257

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)