Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections

Abstract

Cytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as measured from histological tissue sections. Recent work provided the first automatic segmentations of cytoarchitectonic areas in the visual system using Convolutional Neural Networks. We aim to extend this approach to become applicable to a wider range of brain areas, envisioning a solution for mapping the complete human brain. Inspired by recent success in image classification, we propose a contrastive learning objective for encoding microscopic image patches into robust microstructural features, which are efficient for cytoarchitectonic area classification. We show that a model pre-trained using this learning task outperforms a model trained from scratch, as well as a model pre-trained on a recently proposed auxiliary task. We perform cluster analysis in the feature space to show that the learned representations form anatomically meaningful groups.

arXiv commentAccepted to ISBI 2021

References in corpus (6)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• A Simple Framework for Contrastive Learning of Visual Representations
• Large Batch Training of Convolutional Networks
• Supervised Contrastive Learning
• Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks

Cited by in corpus (3)

• Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI
• 2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks
• CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training