An Automatic Tool for Quantification of Nerve Fibers in Corneal Confocal Microscopy Images

• Published in: IEEE transactions on biomedical engineering Vol. 64; no. 4; pp. 786 - 794
• Main Authors: Chen, Xin, Graham, Jim, Dabbah, Mohammad A., Petropoulos, Ioannis N., Tavakoli, Mitra, Malik, Rayaz A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; Biomarkers; Biomedical measurement; Clinical trials; Computer aided diagnosis; Confocal microscopy; Cornea; Cornea - diagnostic imaging; Cornea - innervation; Cornea - pathology; corneal confocal microscopy (CCM); Correlation analysis; Diabetes; Diabetes mellitus; Diabetes mellitus (insulin dependent); Diabetic Neuropathies - diagnostic imaging; Diabetic Neuropathies - pathology; diabetic sensorimotor polyneuropathy (DSPN); Discrete wavelet transforms; Error detection; Feature extraction; Humans; image analysis; Image detection; Image Interpretation, Computer-Assisted - methods; Machine Learning; Medical imaging; Medical research; Microscopy; Microscopy, Confocal - methods; Morphology; Nerve Fibers - pathology; nerve-fiber quantification; Nerves; Patients; Pattern Recognition, Automated - methods; Peripheral neuropathy; Polyneuropathy; Reproducibility of Results; Sensitivity; Sensitivity and Specificity; Sensorimotor system; Software development tools; Training
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2016.2573642

We describe and evaluate an automated software tool for nerve-fiber detection and quantification in corneal confocal microscopy (CCM) images, combining sensitive nerve-fiber detection with morphological descriptors. Method: We have evaluated the tool for quantification of Diabetic Sensorimotor Polyneuropathy (DSPN) using both new and previously published morphological features. The evaluation used 888 images from 176 subjects (84 controls and 92 patients with type 1 diabetes). The patient group was further subdivided into those with (n = 63) and without (n = 29) DSPN. Results: We achieve improved nervefiber detection over previous results (91.7% sensitivity and specificity in identifying nerve-fiber pixels). Automatic quantification of nerve morphology shows a high correlation with previously reported, manually measured, features. Receiver Operating Characteristic (ROC) analysis of both manual and automatic measurement regimes resulted in similar results in distinguishing patients with DSPN from those without: AUC of about 0.77 and 72% sensitivity-specificity at the equal error rate point. Conclusion: Automated quantification of corneal nerves in CCM images provides a sensitive tool for identification of DSPN. Its performance is equivalent to manual quantification, while improving speed and repeatability. Significance: CCM is a novel in vivo imaging modality that has the potential to be a noninvasive and objective image biomarker for peripheral neuropathy. Automatic quantification of nerve morphology is a major step forward in the early diagnosis and assessment of progression, and, in particular, for use in clinical trials to establish therapeutic benefit in diabetic and other peripheral neuropathies.