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