TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds
This work provides a more efficient method for neuroscientists to analyze brain connectivity shapes, but it is incremental as it applies existing deep learning techniques to a specific domain.
The authors tackled the problem of computing shape measures from brain white matter tractography using deep learning, introducing TractShapeNet which outperforms other point cloud models in correlation and error metrics on a dataset of 1065 individuals and enables faster computation compared to conventional tools.
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.