Toward High-Fidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet

2026-07-23

AI Summary

This work addresses key challenges in 3D point cloud learning—namely permutation invariance, lack of local context, difficulty in fine-grained reconstruction, and high computational cost—by introducing a novel paradigm that maps 3D brain surface and fiber point clouds onto a 2D grid domain. Building upon this representation, the authors propose TransUNet, a U-shaped hybrid network that synergistically combines convolutional neural networks with self-attention mechanisms to leverage both local feature extraction and global dependency modeling. This approach effectively mitigates the curse of dimensionality while preserving intricate sulcal and gyral structures. Evaluated on a large-scale finite element simulation dataset comprising 40,401 surface points and 2,382 fiber points, the model achieves high-fidelity prediction of brain folding evolution from initial to final states, significantly outperforming existing methods in both accuracy and structural detail preservation.

Abstract

Learning high-fidelity point-cloud features in the 3D space poses significant challenges, including permutation invariance, lack of local context, difficulty in fine-grained surface reconstruction, and high computational cost. In this article, we propose Trans-Unet, a novel framework that addresses these issues by first tansforming 3D point-cloud data into a 2D grid domain and then employing a U-shaped hybrid model that integrates Convolutional Neural Networks, and self-attention mechanisms. The proposed Trans-Unet effectively learns and reconstructs precise features from high-resolution 3D point-cloud data (with 40,401 points in surface and 2,382 points in fiber) derived from a predefined finite element brain patch growth model, enabling accurate prediction of brain folding patterns. By combining multiple techniques, Trans-Unet leverages the complementary strengths: the 3D-to-2D transformation preserves fine-grained structural information while significantly reducing computational cost and the curse of dimensionality; convolutional blocks capture hierarchical, low-level local representations; and the self-attention mechanism models global, high-level semantics and long-range dependencies. The dataset consists of 3D point-clouds containing both brain surface patches and fiber information generated by a large-scale finite element model. Trans-Unet is applied to predict brain surface folding from the initial state (state 0 or states 0-2) to the final state (state 3). Experimental results demonstrate that Trans-Unet achieves high-resolution predictions of brain patch growth, surpassing existing methods in both fidelity and accuracy.

Problem

• 3D point-cloud
• brain folding morphology
• high-fidelity learning
• morphology prediction
• computational cost

Innovation

• Trans-Unet
• 3D point-cloud learning
• brain folding prediction
• 3D-to-2D transformation
• self-attention mechanism