Automatic analysis of diabetic peripheral neuropathy using multi-scale quantitative morphology of nerve fibres in corneal confocal microscopy imaging

Diabetic peripheral neuropathy (DPN) is one of the most common long term complications of diabetes. Corneal confocal microscopy (CCM) image analysis is a novel non-invasive technique which quantifies corneal nerve fibre damage and enables diagnosis of DPN. This paper presents an automatic analysis and classification system for detecting nerve fibres in CCM images based on a multi-scale adaptive dual-model detection algorithm. The algorithm exploits the curvilinear structure of the nerve fibres and adapts itself to the local image information. Detected nerve fibres are then quantified and used as feature vectors for classification using random forest (RF) and neural networks (NNT) classifiers. We show, in a comparative study with other well known curvilinear detectors, that the best performance is achieved by the multi-scale dual model in conjunction with the NNT classifier. An evaluation of clinical effectiveness shows that the performance of the automated system matches that of ground-truth defined by expert manual annotation.

Corneal confocal microscopy is a new imaging technique with the potential to become a valuable endpoint in the assessment of peripheral neuropathy. For automated analysis of CCM images, weak fibre signals need to be detected against a noisy background. Having recently described a fibre detector based on a combined model of foreground and background at a single scale, we develop a multi-scale version that classifies pixels on the basis of response to the dual model at a range of scales and orientations, using both random forest and neural net classifiers. ROC analysis demonstrates the superior performance of this detector over the single scale version and over a number of well-known linear feature detectors. Comparison with expert manual analysis shows that automatically derived fibre measurement is strongly correlated with manual measurement and produces similar results in stratifying disease.Figure optionsDownload high-quality image (119 K)Download as PowerPoint slideHighlights
► Corneal confocal microscopy is a novel imaging modality for quantifying neuropathy.
► We describe a novel multi-scale detector for low-contrast curvilinear structures.
► We compare it quantitatively with a number of well-known alternative algorithms.
► We evaluate random forest and neural network approaches to pixel classification.
► The automatic quantification of nerve fibres is equivalent to manual measurement.