Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI

Abstract

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, performing classification with minimal annotations, and query-based retrieval of characteristic components of fiber architecture such as U-fibers.

Journal version

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• A Simple Framework for Contrastive Learning of Visual Representations
• Representation Learning with Contrastive Predictive Coding
• Bootstrap your own latent: A new approach to self-supervised Learning
• Albumentations: fast and flexible image augmentations
• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• Recent advances and clinical applications of deep learning in medical image analysis
• Barlow Twins: Self-Supervised Learning via Redundancy Reduction
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
• Self-supervised driven consistency training for annotation efficient histopathology image analysis
• Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
• Self-Supervised Similarity Learning for Digital Pathology
• Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue
• Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections

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