A comprehensive understanding of the organizational principles in the human brain requires, among other factors, well-quantifiable descriptors of nerve fiber architecture. Three-dimensional polarized light imaging (3D-PLI) is a microscopic imaging technique that enables insights into the fine-grained organization of myelinated nerve fibers with high resolution. Descriptors characterizing the fiber architecture observed in 3D-PLI would enable downstream analysis tasks such as multimodal correlation studies, clustering, and mapping. However, best practices for observer-independent characterization of fiber architecture in 3D-PLI are not yet available. To this end, we propose the application of a fully data-driven approach to characterize nerve fiber architecture in 3D-PLI images using self-supervised representation learning. We introduce a 3D-Context Contrastive Learning (CL-3D) objective that utilizes the spatial neighborhood of texture examples across histological brain sections of a 3D reconstructed volume to sample positive pairs for contrastive learning. We combine this sampling strategy with specifically designed image augmentations to gain robustness to typical variations in 3D-PLI parameter maps. The approach is demonstrated for the 3D reconstructed occipital lobe of a vervet monkey brain. We show that extracted features are highly sensitive to different configurations of nerve fibers, yet robust to variations between consecutive brain sections arising from histological processing. We demonstrate their practical applicability for retrieving clusters of homogeneous fiber architecture and performing data mining for interactively selected templates of specific components of fiber architecture such as U-fibers.

翻译：全面理解人脑的组织原则需要（除其他因素外）对神经纤维结构进行良好量化的描述。三维偏振光成像（3D-PLI）是一种显微成像技术，能以高分辨率揭示有髓神经纤维的精细组织结构。能够表征3D-PLI中纤维结构的描述符将支持下游分析任务，如多模态相关性研究、聚类和映射。然而，目前尚缺乏针对3D-PLI纤维结构的无观察者偏倚的最佳表征方法。为此，我们提出采用全数据驱动方法，通过自监督表示学习来表征3D-PLI图像中的神经纤维结构。我们引入了一种三维上下文对比学习（CL-3D）目标函数，利用三维重建体积中组织学脑切片间纹理样本的空间邻域来采样对比学习中的正样本对。我们将此采样策略与专门设计的图像增强相结合，以增强对3D-PLI参数图中典型变化的鲁棒性。该方法在绿猴枕叶三维重建数据集上进行了验证。结果表明，提取的特征对神经纤维的不同构型高度敏感，同时能鲁棒地应对因组织学处理导致的相邻脑切片间的差异。我们展示了这些特征的实际应用价值：可用于检索同质纤维结构聚类，并对交互式选定的特定纤维结构模板（如U型纤维）进行数据挖掘。