Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis

Abstract

Purpose: To describe and evaluate a machine learning-based, automated system to detect exudates and cotton-wool spots in digital color fundus photographs and differentiate them from drusen, for early diagnosis of diabetic retinopathy.

Methods: Three hundred retinal images from one eye of 300 patients with diabetes were selected from a diabetic retinopathy telediagnosis database (nonmydriatic camera, two-field photography): 100 with previously diagnosed bright lesions and 200 without. A machine learning computer program was developed that can identify and differentiate among drusen, (hard) exudates, and cotton-wool spots. A human expert standard for the 300 images was obtained by consensus annotation by two retinal specialists. Sensitivities and specificities of the annotations on the 300 images by the automated system and a third retinal specialist were determined.

Results: The system achieved an area under the receiver operating characteristic (ROC) curve of 0.95 and sensitivity/specificity pairs of 0.95/0.88 for the detection of bright lesions of any type, and 0.95/0.86, 0.70/0.93, and 0.77/0.88 for the detection of exudates, cotton-wool spots, and drusen, respectively. The third retinal specialist achieved pairs of 0.95/0.74 for bright lesions and 0.90/0.98, 0.87/0.98, and 0.92/0.79 per lesion type.

Conclusions: A machine learning-based, automated system capable of detecting exudates and cotton-wool spots and differentiating them from drusen in color images obtained in community based diabetic patients has been developed and approaches the performance level of retinal experts. If the machine learning can be improved with additional training data sets, it may be useful for detecting clinically important bright lesions, enhancing early diagnosis, and reducing visual loss in patients with diabetes.

Figures

Figure 1

Machine learning algorithm steps performed to detect and differentiate ‘bright lesions’. From left to right column, exudates, cotton-wool spots, and drusen. From top to bottom, first row shows the relevant region in the retinal color image (all at the same scale), second row shows the posterior probability map after the first classification step, third row shows the pixel clusters that are probable bright lesions (potential lesions), and the bottom row shows those objects which the system classified as true bright lesions overlaid on the original image.

Figure 2

ROC curves of the automatic system for the different detection tasks. Sensitivity/specificity pairs of Retinal Specialist C are also plotted as points in the graph.

Figure 3

Figure 3A. Example image where experts and automated system agreed on the presence of exudates and cotton-wool spots. As identified by the automated system, the red arrows denote exudates and the green arrow a cotton-wool spot. Figure 3B. Example image where experts and automated system agreed on the presence of drusen. As identified by the automated system, the blue arrows denote drusen. Figure 3C. Example image where experts and automated system did not agree on the presence of drusen or exudates. As identified by the automated system, the blue arrows denote drusen (retinal specialist A and automated system) or exudates (retinal specialist B and C), and the red arrows denote exudates (retinal specialists B and C and automated system)or drusen (retinal specialist A). Figure 3D. Example image where experts and automated system did not agree on the presence of drusen/exudate/cotton-wool spot. As identified by the automated system, the blue arrow denotes a drusen (automated system), exudate (retinal specialists B and C) or no abnormality (retinal specialist A); the green arrow denotes a cotton-wool spot (retinal specialist C and automated system), exudate (retinal specialist B) or no abnormality (retinal specialist A).