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) is widely used in the diagnosis of retinal diseases. Reading OCT images and summarizing its insights is a routine, yet nonetheless time-consuming task. Automatic report generation can alleviate this issue. There are two major challenges in this task: (1) An OCT image may contain several fundus abnormalities and it is difficult to detect them all simultaneously. (2) The diagnostic reports are complex, which need to describe multiple lesions. In this paper, we propose a deep learning-based model, named as VSTA model (Visual and Semantic Topic Attention model), which is able to generate report from the input OCT image. Our major contributions include: (1) Semantic attention and visual attention are jointly embedded to the model to generate diagnosis report with complex content. (2) Semantic tags based on image similarity is employed to initialize the semantic attention weights, which increases the prediction accuracy of the model. With the proposed VSTA model, the metric of BLEU-4, CIDEr and ROUGE-L reach 31.16, 264.22 and 52.58, which are better than some existing advanced methods.
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