DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

Abstract

Parcellation of whole-brain tractography streamlines is an important step for tract-based analysis of brain white matter microstructure. Existing fiber parcellation approaches rely on accurate registration between an atlas and the tractograms of an individual, however, due to large individual differences, accurate registration is hard to guarantee in practice. To resolve this issue, we propose a novel deep learning method, called DeepBundle, for registration-free fiber parcellation. Our method utilizes graph convolution neural networks (GCNNs) to predict the parcellation label of each fiber tract. GCNNs are capable of extracting the geometric features of each fiber tract and harnessing the resulting features for accurate fiber parcellation and ultimately avoiding the use of atlases and any registration method. We evaluate DeepBundle using data from the Human Connectome Project. Experimental results demonstrate the advantages of DeepBundle and suggest that the geometric features extracted from each fiber tract can be used to effectively parcellate the fiber tracts.

8 pages

References in corpus (5)

• Geometric deep learning: going beyond Euclidean data
• Spectral Networks and Locally Connected Networks on Graphs
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
• TractSeg - Fast and accurate white matter tract segmentation
• The White Matter Query Language: A Novel Approach for Describing Human White Matter Anatomy

Cited by in corpus (3)

• Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review
• TractoEmbed: Modular Multi-level Embedding framework for white matter tract segmentation
• Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging