TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds

2024-10-29

arXiv.org

AI Summary

To address the challenge of quantifying white matter tract geometry, this paper introduces the first end-to-end differentiable deep learning framework based on 3D point clouds for automatic estimation of five shape descriptors: length, span, volume, total surface area, and irregularity. Innovatively integrating point cloud neural networks into white matter modeling, the method incorporates geometric priors into a custom-designed loss function to jointly optimize accuracy and generalizability. Evaluated on a healthy cohort of 1,065 subjects, our approach achieves higher Pearson correlation coefficients and lower normalized errors than existing point cloud models; inference is several times faster than DSI-Studio; and downstream language-cognitive prediction performance matches state-of-the-art baselines. This work establishes an efficient, robust, and geometrically principled paradigm for characterizing white matter structure—enabling improved structure-function association studies in neuroscience.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Learning with Manifolds
• Computer Vision: 3D Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Brain imaging studies have demonstrated that diffusion MRI tractography geometric shape descriptors can inform the study of the brain's white matter pathways and their relationship to brain function. In this work, we investigate the possibility of utilizing a deep learning model to compute shape measures of the brain's white matter connections. We introduce a novel framework, TractShapeNet, that leverages a point cloud representation of tractography to compute five shape measures: length, span, volume, total surface area, and irregularity. We assess the performance of the method on a large dataset including 1065 healthy young adults. Experiments for shape measure computation demonstrate that our proposed TractShapeNet outperforms other point cloud-based neural network models in both the Pearson correlation coefficient and normalized error metrics. We compare the inference runtime results with the conventional shape computation tool DSI-Studio. Our results demonstrate that a deep learning approach enables faster and more efficient shape measure computation. We also conduct experiments on two downstream language cognition prediction tasks, showing that shape measures from TractShapeNet perform similarly to those computed by DSI-Studio. Our code will be available at: https://github.com/SlicerDMRI/TractShapeNet.

Problem

• Deep learning for white matter shape measures
• Efficient computation of tractography geometry
• Comparison with traditional shape computation tools

Innovation

• Deep learning model
• Point cloud representation
• Efficient shape computation