Branch retinal artery occlusion (BRAO) is an ophthalmic emergency. Acute BRAO is a clinical manifestation of BRAO. Due to its various shapes, locations and the blurred boundary, the automatic segmentation of acute BRAO is very challenging. To tackle these problems, we propose a novel method based on deep learning for automatic acute BRAO segmentation in optical coherence tomography (OCT) image. In this method, a novel Bayes posterior attention network, named as BPANet, is proposed for precise segmentation of the lesion. Our major contributions include: (1) A novel Bayes posterior probability based spatial attention module is used to enhance the information of lesion region. (2) An effective max-pooling and average-pooling channel attention module is embedded into BPANet to improve the effectiveness of the feature extraction. The proposed method is evaluated on 472 OCT B-scan images with a 4-fold cross validation strategy. The mean and standard deviation of Dice similarity coefficient, true positive rate, accuracy and intersection over union are 85.48±1.75%, 88.84±1.19%, 98.63±0.48% and 76.88±2.92%, respectively. The primary results show the effectiveness of the proposed method.
In retinal optical coherence tomography (OCT), it is significant to focus the scan beam on the retina layers of the human eye fast to acquire high-quality OCT images. The refractive errors of the human eye are usually compensated by shifting the optical lens in the sample arm manually or electrically. Otherwise, refractive errors of the human eye will cause axial focal shift of the scanning beam and blur the OCT images. We propose a fast automatic focus method using the interference fringes magnitudes of the whole retinal layers to determine the best refractive correction position of motor-driven optical compensation lens without the aid of optical fundus imaging or OCT B-scan imaging. Also, the optical path difference between the sample arm and reference arm is adjusted in the spectral-domain OCT (SD-OCT) to reduce the effects of sensitivity roll-off. The experiments in the lab-built SD-OCT show that the average running time of the method is 15.2 s and the mean absolute deviation of each imaging is within 1 mm. The method is independent of extra optical setup and requires less computational consumption, which is suitable for low-cost and automatic retinal OCT devices.
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