利用对比学习与几何展开分析3D-PLI中人脑海马的区域组织
Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding
AI总结：
本文提出结合几何展开与自监督对比学习深度纹理特征的新方法，用于分析3D-PLI人脑海马区域组织，并验证所得簇与经典海马亚区描述一致。
摘要：
理解人脑的皮层组织需要为不同的结构和功能成像数据提供可解释的描述符。3D偏振光成像（3D-PLI）是一种以高分辨率可视化死后大脑纤维结构的成像模态，同时也能捕捉细胞体的存在，例如用于识别海马亚区。然而，3D-PLI图像中丰富的纹理使该模态特别难以分析，表征结构模式的最佳实践仍有待建立。在这项工作中，我们展示了一种新方法，通过将展开方法的最新进展与使用自监督对比学习方法获得的深度纹理特征相结合，来分析3D-PLI中人脑海马的区域组织。我们在表示中识别出与经典海马亚区描述高度吻合的簇，从而验证了所开发方法的有效性。
英文摘要：
Understanding the cortical organization of the human brain requires interpretable descriptors for distinct structural and functional imaging data. 3D polarized light imaging (3D-PLI) is an imaging modality for visualizing fiber architecture in postmortem brains with high resolution that also captures the presence of cell bodies, for example, to identify hippocampal subfields. The rich texture in 3D-PLI images, however, makes this modality particularly difficult to analyze and best practices for characterizing architectonic patterns still need to be established. In this work, we demonstrate a novel method to analyze the regional organization of the human hippocampus in 3D-PLI by combining recent advances in unfolding methods with deep texture features obtained using a self-supervised contrastive learning approach. We identify clusters in the representations that correspond well with classical descriptions of hippocampal subfields, lending validity to the developed methodology.
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