Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Comput Methods Programs Biomed. 2024 Nov;256:108368. doi: 10.1016/j.cmpb.2024.108368. Epub 2024 Aug 12.
Parkinson's disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates that changes in pathology, such as the texture and thickness of the retinal layers, can serve as biomarkers for clinical PD diagnosis based on optical coherence tomography (OCT) images. However, the pathological manifestations of PD in the retinal layers are subtle compared to the more salient lesions associated with retinal diseases.
Inspired by textural edge feature extraction in frequency domain learning, we aim to explore a potential approach to enhance the distinction between the feature distributions in retinal layers of PD cases and healthy controls. In this paper, we introduce a simple yet novel wavelet-based selection and recalibration module to effectively enhance the feature representations of the deep neural network by aggregating the unique clinical properties, such as the retinal layers in each frequency band. We combine this module with the residual block to form a deep network named Wavelet-based Selection and Recalibration Network (WaveSRNet) for automatic PD screening.
The extensive experiments on a clinical PD-OCT dataset and two publicly available datasets demonstrate that our approach outperforms state-of-the-art methods. Visualization analysis and ablation studies are conducted to enhance the explainability of WaveSRNet in the decision-making process.
Our results suggest the potential role of the retina as an assessment tool for PD. Visual analysis shows that PD-related elements include not only certain retinal layers but also the location of the fovea in OCT images.
帕金森病（PD）是全球最常见的神经退行性脑部疾病之一。因此，准确的 PD 筛查对于早期临床干预和治疗至关重要。最近的临床研究表明，基于光学相干断层扫描（OCT）图像，视网膜层的病理学变化，如纹理和厚度的变化，可以作为临床 PD 诊断的生物标志物。然而，与视网膜疾病相关的更明显病变相比，PD 在视网膜层中的病理表现较为微妙。
受频域学习中纹理边缘特征提取的启发，我们旨在探索一种潜在的方法，以增强 PD 病例和健康对照组的视网膜层特征分布之间的区分度。在本文中，我们引入了一个简单而新颖的基于小波的选择和重新校准模块，通过聚合独特的临床特性（例如每个频带的视网膜层），有效地增强了深度神经网络的特征表示。我们将该模块与残差块结合，形成一个名为基于小波的选择和重新校准网络（WaveSRNet）的深度网络，用于自动 PD 筛查。
我们在一个临床 PD-OCT 数据集和两个公开可用的数据集上进行了广泛的实验，结果表明我们的方法优于最先进的方法。进行了可视化分析和消融研究，以增强 WaveSRNet 在决策过程中的可解释性。
我们的结果表明视网膜作为 PD 评估工具的潜力。视觉分析表明，PD 相关的元素不仅包括某些视网膜层，还包括 OCT 图像中黄斑的位置。
