用于增强3D OCT影像青光眼诊断的空间感知Transformer-GRU框架
Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging
AI总结：
本研究针对青光眼早期检测需求，提出融合预训练Vision Transformer与双向门控循环单元的空间感知Transformer-GRU框架，利用3D OCT影像实现自动化青光眼诊断，在大型数据集上性能优于现有最优方法，具备临床应用潜力。
摘要：
青光眼是导致不可逆失明的主要原因，亟需早期检测以实现准确及时的干预，防止不可逆的视力丧失。本研究提出一种新型深度学习框架，利用3D光学相干断层扫描（OCT）影像的诊断价值实现自动化青光眼检测。该框架整合了在视网膜数据上预训练的Vision Transformer以进行丰富的切片级特征提取，以及双向门控循环单元（Gated Recurrent Unit）用于捕捉切片间的空间依赖关系。这种双组件方法能够全面分析局部细微特征与全局结构完整性，这对准确的青光眼诊断至关重要。在大型数据集上的实验结果表明，所提方法的性能优于现有最优方法，取得了93.01%的F1分数、69.33%的马修斯相关系数（MCC）以及94.20%的AUC。该框架挖掘3D OCT数据中宝贵信息的能力，对于增强临床决策支持系统、改善青光眼管理中的患者预后具有重大潜力。
英文摘要：
Glaucoma, a leading cause of irreversible blindness, necessitates early detection for accurate and timely intervention to prevent irreversible vision loss. In this study, we present a novel deep learning framework that leverages the diagnostic value of 3D Optical Coherence Tomography (OCT) imaging for automated glaucoma detection. In this framework, we integrate a pre-trained Vision Transformer on retinal data for rich slice-wise feature extraction and a bidirectional Gated Recurrent Unit for capturing inter-slice spatial dependencies. This dual-component approach enables comprehensive analysis of local nuances and global structural integrity, crucial for accurate glaucoma diagnosis. Experimental results on a large dataset demonstrate the superior performance of the proposed method over state-of-the-art ones, achieving an F1-score of 93.01%, Matthews Correlation Coefficient (MCC) of 69.33%, and AUC of 94.20%. The framework's ability to leverage the valuable information in 3D OCT data holds significant potential for enhancing clinical decision support systems and improving patient outcomes in glaucoma management.
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