 Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 \mu 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  .
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 .
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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 impairment and blindness.
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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
MAs, a potential marker that might dictate MA pathology and affect the progression of DR
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previous
the numerous
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MA detection on standard
assessing the number of MAs on
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policy, including preprocessing, data augmentation and transfer learning,
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demonstrating very accurate MA detection and segmentation, leading to correct MA morphological classification, while outperforming the
and our results demonstrate that the proposed model outperforms the
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segmentation model
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and enables correct MA morphological classification