8 research outputs found
Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study
Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to
blindness and cardiovascular disease. Information about early stage T2D might
be present in retinal fundus images, but to what extent these images can be
used for a screening setting is still unknown. In this study, deep neural
networks were employed to differentiate between fundus images from individuals
with and without T2D. We investigated three methods to achieve high
classification performance, measured by the area under the receiver operating
curve (ROC-AUC). A multi-target learning approach to simultaneously output
retinal biomarkers as well as T2D works best (AUC = 0.746 [0.001]).
Furthermore, the classification performance can be improved when images with
high prediction uncertainty are referred to a specialist. We also show that the
combination of images of the left and right eye per individual can further
improve the classification performance (AUC = 0.758 [0.003]), using a
simple averaging approach. The results are promising, suggesting the
feasibility of screening for T2D from retinal fundus images.Comment: to be published in the proceeding of SPIE - Medical Imaging 2020, 6
pages, 1 figur
Brain-inspired algorithms for retinal image analysis
\u3cp\u3eRetinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck project, a large-scale screening program for diabetic retinopathy and other retinal diseases in Northeast China. The paper discusses the theory of orientation scores, inspired by cortical multi-orientation pinwheel structures, and presents applications for automated quality assessment, optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power.\u3c/p\u3
Brain-inspired algorithms for retinal image analysis
Retinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck project, a large-scale screening program for diabetic retinopathy and other retinal diseases in Northeast China. The paper discusses the theory of orientation scores, inspired by cortical multi-orientation pinwheel structures, and presents applications for automated quality assessment, optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power