Next Article in Journal
Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer
Previous Article in Journal
METER.AC: Live Open Access Atmospheric Monitoring Data for Bulgaria with High Spatiotemporal Resolution
Previous Article in Special Issue
The Eminence of Co-Expressed Ties in Schizophrenia Network Communities
Open AccessArticle

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

School of Computer Science, The University of Sydney, NSW 2006, Australia
School of Optometry and Vision Science, University of New South Wales, Sydney, NSW 2052, Australia
Author to whom correspondence should be addressed.
Received: 20 February 2020 / Revised: 4 April 2020 / Accepted: 8 April 2020 / Published: 14 April 2020
(This article belongs to the Special Issue Data-Driven Healthcare Tasks: Tools, Frameworks, and Techniques)
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. View Full-Text
Keywords: U-Net; deep learning; corneal nerve; automatic analysis; tortuosity U-Net; deep learning; corneal nerve; automatic analysis; tortuosity
Show Figures

Figure 1

MDPI and ACS Style

Mehrgardt, P.; Zandavi, S.M.; Poon, S.K.; Kim, J.; Markoulli, M.; Khushi, M. U-Net Segmented Adjacent Angle Detection (USAAD) for Automatic Analysis of Corneal Nerve Structures. Data 2020, 5, 37.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop