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

06/05/2021 ∙ by Qian Zhang, et al. ∙ 21

Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 μ m. This technique enables detection of the morphologies of individual microaneurysms (MAs), which are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness. In contrast to previous automatic models developed for MA detection on standard 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 policy, including preprocessing, data augmentation and transfer learning, to automatically segment MAs from AOSLO images. We evaluate the performance of AOSLO-net using 87 DR AOSLO images demonstrating very accurate MA detection and segmentation, leading to correct MA morphological classification, while outperforming the state-of-the-art both in accuracy and cost.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 8

page 9

page 10

page 29

page 30

page 31

page 32

page 33

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.