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Microaneurysms (MAs) are one of the earliest signs of diabetic retinopathy(DR), a frequent complication of diabetes that can lead to visual impairmentand blindness. Adaptive optics scanning laser ophthalmoscopy (AOSLO) providesreal-time retinal images with resolution down to 2 $\mu m$ and thus allowsdetection of the morphologies of individual MAs, a potential marker that mightdictate MA pathology and affect the progression of DR. In contrast to thenumerous automatic models developed for assessing the number of MAs on fundusphotographs, currently there is no high throughput image protocol available forautomatic analysis of AOSLO photographs. To address this urgency, we introduceAOSLO-net, a deep neural network framework with customized training policies toautomatically segment MAs from AOSLO images. We evaluate the performance ofAOSLO-net using 87 DR AOSLO images and our results demonstrate that theproposed model outperforms the state-of-the-art segmentation model both inaccuracy and cost and enables correct MA morphological classification.

AOSLO-net: A deep learning-based method for automatic segmentation of  retinal microaneurysms from adaptive optics scanning laser ophthalmoscope  images