Reconnection of Interrupted Curvilinear Structures via Cortically Inspired Completion for Ophthalmologic Images

Aims/hypothesis Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics. Methods Our deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy. Uazman Alam and Yalin Zheng are joint senior authors.

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Retinal vascular analysis: Segmentation, tracing, and beyond

Cheng

Lyu

Zhao

et al. 2019

Computational Retinal Image Analysis

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Reconnection of Interrupted Curvilinear Structures via Cortically Inspired Completion for Ophthalmologic Images

Abstract: The presented method is a critical work to obtain more complete curvilinear structures in ophthalmologic images. It provides better topological and geometric connectivities for further analysis.

Cited by 13 publications

References 44 publications

Restoring Connectivity in Vascular Segmentations Using a Learned Post-processing Model

Restoring Connectivity in Vascular Segmentations Using a Learned Post-processing Model

An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study

Retinal vascular analysis: Segmentation, tracing, and beyond

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