EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection

Venkatesh Krishna Adithya,Bryan M Williams,Silvester Czanner,Colin E Willoughby,David S Friedman,Gabriela Czanner,Rengaraj Venkatesh,Srinivasan Kavitha

doi:10.3390/jimaging7060092

Abstract

Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed “SpaGen” We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, “EffUnet-SpaGen”, is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings.

Highlights

Glaucoma is a neurodegenerative disease resulting in progressive optic nerve damage with a characteristic pattern of optic nerve damage and visual field loss
We demonstrate the performance generative model spatial generative algorithm (SpaGen), which takes into account the extracted profile and the cup to of our algorithm two large publicly available datasets and the show how it can beofquickly disc area ratio toon improve detection; (4) We demonstrate performance our algorithm recalibrated for independent data, by recalibrating the EffUnet layer only
EffUnet-SpaGen, on the ORIGA dataset has the best performance published to date (AUROC = 0.997) when compared to state-of-art architectures (Table 6)

Summary

Introduction

Glaucoma is a neurodegenerative disease resulting in progressive optic nerve damage with a characteristic pattern of optic nerve damage and visual field loss. Late diagnosis is a major risk factor for permanent visual loss [1], and early glaucoma detection is key to preventing avoidable blindness. Detection of structural changes to the optic nerve using imaging or clinical examination is central to diagnosis but challenging even for highly skilled specialists. Patients can be misclassified, which is a significant challenge, especially in low-resource settings where access to clinical expertise and specialist diagnostic equipment is limited. A low-cost and accurate automated method of quantifying glaucomatous structural changes would help meet this need [2]. A significant challenge of developing automated glaucoma detection algorithms is that a vast number of labelled colour fundus images is required for training (Figure 1).

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Conclusion