Segmentation and multiparametric evaluation of corneal whorl-like nerves for in vivo confocal microscopy images in dry eye disease.

Objective: To establish an automated corneal nerve analysis system for corneal in vivo confocal microscopy (IVCM) images from both the whorl-like corneal nerves in the inferior whorl (IW) region and the straight ones in the central cornea and to characterise the geometric features of cornea nerves in dry eye disease (DED).

Methods and analysis: An encoder-decoder-based semi-supervised method was proposed for corneal nerve segmentation. This model's performance was compared with the ground truth provided by experienced clinicians, using Dice similarity coefficient (DSC), mean intersection over union (mIoU), accuracy (Acc), sensitivity (Sen) and specificity (Spe). The corneal nerve total length (CNFL), tortuosity (CNTor), fractal dimension (CND_f) and number of branching points (CNBP) were used for further analysis in an independent DED dataset including 50 patients with DED and 30 healthy controls.

Results: The model achieved 95.72% Acc, 97.88% Spe, 80.61% Sen, 75.26% DSC, 77.57% mIoU and an area under the curve value of 0.98. For clinical evaluation, the CNFL, CNBP and CND_f for whorl-like and straight nerves showed a significant decrease in DED patients compared with healthy controls (p<0.05). Additionally, significantly elevated CNTor was detected in the IW in DED patients (p<0.05). The CNTor for straight corneal nerves, however, showed no significant alteration in DED patients (p>0.05).

Conclusion: The proposed method segments both whorl-like and straight corneal nerves in IVCM images with high accuracy and offered parameters to objectively quantify DED-induced corneal nerve injury. The IW is an effective region to detect alterations of multiple geometric indices in DED patients.