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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
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Bundle geodesic convolutional neural network for diffusion-weighted imaging segmentation.
Renfei Liu1, François Lauze1, Kenny Erleben1
1University of Copenhagen, Department of Computer Science, Copenhagen, Denmark.
Journal of Medical Imaging (Bellingham, Wash.)
|November 21, 2022
Summary
This study introduces a Riemannian deep learning framework for analyzing diffusion-weighted imaging (DWI) data, enabling effective tissue classification with limited training data and fewer model parameters.
Area of Science:
- Medical Imaging
- Machine Learning
- Computational Neuroscience
Background:
- Machine learning on diffusion-weighted imaging (DWI) data is hindered by large sample sizes and limited labeled data.
- Leveraging the inherent geometry of DWI data allows for learning patterns from minimal training sets.
Purpose of the Study:
- To develop a novel Riemannian deep learning framework for tissue classification using single-shell DWI data.
- To address the challenges of data size and scarcity of labels in DWI analysis.
Main Methods:
- A three-layer framework: lifting layer for local representation on tangent spaces, group convolution layer for rotation kernel convolution, and projection layer for manifold-based function formation.
- The approach utilizes the geometry of DWI data within a Riemannian deep learning architecture.
Main Results:
- The proposed method achieves state-of-the-art performance with significantly fewer model parameters.
- Sensitivity analysis demonstrated that reduced training data (down to 29.4%) mildly impacts overall accuracy but improves minority class performance.
Conclusions:
- This work extends convolutional neural networks to Riemannian manifolds, offering a powerful tool for brain structural pattern analysis.
- The framework shows potential for aiding manual data annotation in neuroimaging.
