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.
Optical coherence tomography (OCT), a non-invasive high-resolution imaging technology of retinal tissues, has been widely used in the diagnosis of retinal diseases. However, the shortage of ophthalmologists and the overloaded work have caused great difficulties in screening for retinal diseases. Therefore, developing an accurate automatic diagnosis system for screening retinal diseases in OCT images is essential for the prevention and treatment of retinal diseases. To this end, we propose a novel multi-view-based automatic aided diagnosis method for simultaneously screening multiple diseases in retinal OCT images. First, we collected 11,211 cases of 11 common retinal diseases from the ophthalmology clinic, and each case included two OCTs acquired from different views. Then, to automatically and accurately screen diseases in retinal OCT images, a novel multi-view attention network is proposed for screening retinal diseases based on the collected data. Finally, we conduct experiments based on the collected clinical data to evaluate the performance of the proposed method. The AUC of the proposed method achieves 0.9023, which indicates the effectiveness of the proposed method.
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