Han Kangfu, Hu Dan, Cheng Jiale, Liu Tianming, Bozoki Andrea, Zhu Dajiang, Li Gang
Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
School of Computing, University of Georgia, Athens, GA, USA.
Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10981287. Epub 2025 May 12.
In structural magnetic resonance imaging (MRI), morphological connectome plays an important role in capturing coordinated patterns of region-wise morphological features for brain disorder diagnosis. However, significant challenges remain in aggregating diverse representations from multiple brain atlases, stemming from variations in the definition of regions of interest. To effectively integrate complementary information from multiple atlases while mitigating possible biases, we propose a novel dual multi-atlas representation alignment approach (DMAA) for brain disorder diagnosis. Specifically, we first minimize the maximum mean discrepancy of multi-atlas representations to align them into a unified distribution, reducing inter-atlas variability and enhancing effective feature fusion. Then, to further manage the anatomical variability, we apply optimal transport to capture and harmonize region-wise differences, preserving plausible relationships across atlases. Extensive experiments on ADNI, PPMI, ADHD200, and SchizConnect datasets demonstrate the effectiveness of our proposed DMAA on brain disorder diagnosis using multi-atlas morphological connectome.
在结构磁共振成像(MRI)中,形态连接组在捕捉区域形态特征的协调模式以用于脑部疾病诊断方面发挥着重要作用。然而,由于感兴趣区域定义的差异,在聚合来自多个脑图谱的不同表示时仍存在重大挑战。为了有效整合来自多个图谱的互补信息,同时减轻可能的偏差,我们提出了一种用于脑部疾病诊断的新型双多图谱表示对齐方法(DMAA)。具体而言,我们首先最小化多图谱表示的最大均值差异,将它们对齐到统一分布,减少图谱间的变异性并增强有效特征融合。然后,为了进一步处理解剖变异性,我们应用最优传输来捕捉并协调区域差异,保留图谱间合理的关系。在ADNI、PPMI、ADHD200和SchizConnect数据集上进行的大量实验证明了我们提出的DMAA在使用多图谱形态连接组进行脑部疾病诊断方面的有效性。