TreeMoCo: Contrastive Neuron Morphology Representation Learning

Hanbo Chen, Jiawei Yang, Daniel Maxim Iascone, Lijuan Liu, Lei He, Hanchuan Peng, Jianhua Yao

Keywords: neuron morphology, cell types, contrastive learning, tree graph augmentation, tree-LSTM

Abstract: Morphology of neuron trees is a key indicator to delineate neuronal cell-types, analyze brain development process, and evaluate pathological changes in neurological diseases. Traditional analysis mostly relies on heuristic features and visual inspections. A quantitative, informative, and comprehensive representation of neuron morphology is largely absent but desired. To fill this gap, in this work, we adopt a Tree-LSTM network to encode neuron morphology and introduce a self-supervised learning framework named TreeMoCo to learn features without the need for labels. We test TreeMoCo on 2403 high-quality 3D neuron reconstructions of mouse brains from three different public resources. Our results show that TreeMoCo is effective in both classifying major brain cell-types and identifying sub-types. To our best knowledge, TreeMoCo is the very first to explore learning the representation of neuron tree morphology with contrastive learning. It has a great potential to shed new light on quantitative neuron morphology analysis. Code is available at https://github.com/TencentAILabHealthcare/NeuronRepresentation.

TL;DR: This paper introduce TreeMoCo, an early attempt of self-supervised contrastive learning framework for morphology representation of neuron tree.