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- W4221077029 abstract "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." @default.
- W4221077029 created "2022-04-03" @default.
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- W4221077029 date "2022-04-04" @default.
- W4221077029 modified "2023-09-29" @default.
- W4221077029 title "Acute branch retinal artery occlusion segmentation based on Bayes posterior probability and deep learning" @default.
- W4221077029 doi "https://doi.org/10.1117/12.2611500" @default.
- W4221077029 hasPublicationYear "2022" @default.
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