U-Net Segmented Adjacent Angle Detection (USAAD) for Automatic Analysis of Corneal Nerve Structures

Philip Mehrgardt, Seid Miad Zandavi, Simon K. Poon, Juno Kim, Maria Markoulli and Matloob Khushi
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Philip Mehrgardt: School of Computer Science, The University of Sydney, NSW 2006, Australia
Seid Miad Zandavi: School of Computer Science, The University of Sydney, NSW 2006, Australia
Simon K. Poon: School of Computer Science, The University of Sydney, NSW 2006, Australia
Juno Kim: School of Optometry and Vision Science, University of New South Wales, Sydney, NSW 2052, Australia
Maria Markoulli: School of Optometry and Vision Science, University of New South Wales, Sydney, NSW 2052, Australia
Matloob Khushi: School of Computer Science, The University of Sydney, NSW 2006, Australia

Data, 2020, vol. 5, issue 2, 1-19

Abstract: Measurement of corneal nerve tortuosity is associated with dry eye disease, diabetic retinopathy, and a range of other conditions. However, clinicians measure tortuosity on very different grading scales that are inherently subjective. Using in vivo confocal microscopy, 253 images of corneal nerves were captured and manually labelled by two researchers with tortuosity measurements ranging on a scale from 0.1 to 1.0. Tortuosity was estimated computationally by extracting a binarised nerve structure utilising a previously published method. A novel U-Net segmented adjacent angle detection (USAAD) method was developed by training a U-Net with a series of back feeding processed images and nerve structure vectorizations. Angles between all vectors and segments were measured and used for training and predicting tortuosity measured by human labelling. Despite the disagreement among clinicians on tortuosity labelling measures, the optimised grading measurement was significantly correlated with our USAAD angle measurements. We identified the nerve interval lengths that optimised the correlation of tortuosity estimates with human grading. We also show the merit of our proposed method with respect to other baseline methods that provide a single estimate of tortuosity. The real benefit of USAAD in future will be to provide comprehensive structural information about variations in nerve orientation for potential use as a clinical measure of the presence of disease and its progression.

Keywords: U-Net; deep learning; corneal nerve; automatic analysis; tortuosity (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
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