Liu Kai, Zhang Jicong
School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China.
Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100083, China.
Biomed Opt Express. 2024 Feb 2;15(3):1370-1392. doi: 10.1364/BOE.512138. eCollection 2024 Mar 1.
Currently, deep learning-based methods have achieved success in glaucoma detection. However, most models focus on OCT images captured by a single scan pattern within a given region, holding the high risk of the omission of valuable features in the remaining regions or scan patterns. Therefore, we proposed a multi-region and multi-scan-pattern fusion model to address this issue. Our proposed model exploits comprehensive OCT images from three fundus anatomical regions (macular, middle, and optic nerve head regions) being captured by four scan patterns (radial, volume, single-line, and circular scan patterns). Moreover, to enhance the efficacy of integrating features across various scan patterns within a region and multiple regional features, we employed an attention multi-scan fusion module and an attention multi-region fusion module that auto-assign contribution to distinct scan-pattern features and region features adapting to characters of different samples, respectively. To alleviate the absence of available datasets, we have collected a specific dataset (MRMSG-OCT) comprising OCT images captured by four scan patterns from three regions. The experimental results and visualized feature maps both demonstrate that our proposed model achieves superior performance against the single scan-pattern models and single region-based models. Moreover, compared with the average fusion strategy, our proposed fusion modules yield superior performance, particularly reversing the performance degradation observed in some models relying on fixed weights, validating the efficacy of the proposed dynamic region scores adapted to different samples. Moreover, the derived region contribution scores enhance the interpretability of the model and offer an overview of the model's decision-making process, assisting ophthalmologists in prioritizing regions with heightened scores and increasing efficiency in clinical practice.
目前，基于深度学习的方法在青光眼检测方面取得了成功。然而，大多数模型专注于给定区域内通过单一扫描模式捕获的OCT图像，存在遗漏其余区域或扫描模式中有价值特征的高风险。因此，我们提出了一种多区域和多扫描模式融合模型来解决这个问题。我们提出的模型利用了来自三个眼底解剖区域（黄斑、中间和视神经乳头区域）的综合OCT图像，这些图像由四种扫描模式（径向、容积、单线和圆形扫描模式）捕获。此外，为了提高整合区域内各种扫描模式特征和多个区域特征的效果，我们采用了注意力多扫描融合模块和注意力多区域融合模块，它们分别根据不同样本的特征自动为不同的扫描模式特征和区域特征分配贡献。为了缓解可用数据集的缺乏，我们收集了一个特定的数据集（MRMSG-OCT），该数据集包含由三种扫描模式从三个区域捕获的OCT图像。实验结果和可视化特征图均表明，我们提出的模型相对于单扫描模式模型和单区域模型具有卓越的性能。此外，与平均融合策略相比，我们提出的融合模块具有卓越的性能，特别是扭转了一些依赖固定权重的模型中观察到的性能下降，验证了所提出的适应不同样本的动态区域分数的有效性。此外，导出的区域贡献分数提高了模型的可解释性，并提供了模型决策过程的概述，有助于眼科医生优先处理分数较高的区域并提高临床实践效率。
