A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography.

ABSTRACT: Recently, shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and clinical phenotypes. However, conventional methods for computing shape measures are computationally expensive and time-consuming for large-scale datasets due to reliance on voxel-based representations. To address these limitations, we introduce Tract2Shape, a novel multimodal deep learning framework that integrates geometric streamline features (as point clouds) with scalar data descriptors (as tabular data) from tractography to predict 10 white matter tractography shape measures. We propose a Siamese architecture in which each subnetwork incorporates a dual-encoder design, enabling each encoder to learn

SUBMITTER: Lo Y 

PROVIDER: S-EPMC12576896 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature