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.

应用自我监督表示学习的完全数据驱动方法，以 3D-PLI 图像为基础，对神经纤维结构进行特征化。