Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada; Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
Comput Med Imaging Graph. 2023 Sep;108:102279. doi: 10.1016/j.compmedimag.2023.102279. Epub 2023 Jul 29.
Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder characterized by motor neuron degeneration. Significant research has begun to establish brain magnetic resonance imaging (MRI) as a potential biomarker to diagnose and monitor the state of the disease. Deep learning has emerged as a prominent class of machine learning algorithms in computer vision and has shown successful applications in various medical image analysis tasks. However, deep learning methods applied to neuroimaging have not achieved superior performance in classifying ALS patients from healthy controls due to insignificant structural changes correlated with pathological features. Thus, a critical challenge in deep models is to identify discriminative features from limited training data. To address this challenge, this study introduces a framework called SFFormer, which leverages the power of the vision transformer architecture to distinguish ALS subjects from the control group by exploiting the long-range relationships among image features. Additionally, spatial and frequency domain information is combined to enhance the network's performance, as MRI scans are initially captured in the frequency domain and then converted to the spatial domain. The proposed framework is trained using a series of consecutive coronal slices and utilizes pre-trained weights from ImageNet through transfer learning. Finally, a majority voting scheme is employed on the coronal slices of each subject to generate the final classification decision. The proposed architecture is extensively evaluated with multi-modal neuroimaging data (i.e., T1-weighted, R2*, FLAIR) using two well-organized versions of the Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The experimental results demonstrate the superiority of the proposed strategy in terms of classification accuracy compared to several popular deep learning-based techniques.
肌萎缩侧索硬化症（ALS）是一种复杂的神经退行性疾病，其特征是运动神经元退化。大量研究已经开始将脑磁共振成像（MRI）确立为一种潜在的生物标志物，用于诊断和监测疾病状态。深度学习已成为计算机视觉中一种重要的机器学习算法类别，并在各种医学图像分析任务中取得了成功的应用。然而，由于与病理特征相关的结构变化不明显，深度学习方法在将 ALS 患者与健康对照者进行分类方面并未取得优异的性能。因此，深度学习模型面临的一个关键挑战是从有限的训练数据中识别出有区别的特征。为了解决这一挑战，本研究引入了一种称为 SFFormer 的框架，该框架利用视觉Transformer 架构的强大功能，通过利用图像特征之间的远程关系，将 ALS 患者与对照组区分开来。此外，空间和频域信息被结合起来以提高网络的性能，因为 MRI 扫描最初是在频域中捕获的，然后转换到空间域。所提出的框架使用一系列连续的冠状切片进行训练，并通过迁移学习利用来自 ImageNet 的预训练权重。最后，对每个受试者的冠状切片采用多数投票方案生成最终的分类决策。所提出的架构使用加拿大 ALS 神经影像学联合会（CALSNIC）多中心数据集的两个组织良好的版本的多模态神经影像学数据（即 T1 加权、R2*、FLAIR）进行了广泛评估。实验结果表明，与几种流行的基于深度学习的技术相比，所提出的策略在分类准确性方面具有优越性。
