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

The formation of brain folds (gyri and sulci) is driven by complex biological and mechanical processes. Early models like finite element simulations captured tissue mechanics but lacked resolution and individual variability. Deep learning approaches, including PointNet and Transformer-based models, have shown promise but face challenges in high-resolution, detailed brain modeling. Combining physical models with data-driven methods is an emerging trend, yet effective high-res point cloud learning remains unresolved due to issues like permutation invariance, local detail preservation, and computational efficiency.

Accurately predicting brain folding patterns from high-resolution point clouds is hindered by the unordered, sparse, and non-unique nature of data. Existing models struggle to balance local detail capture and global dependency modeling, especially at high resolutions. These limitations impede precise, scalable, and generalizable brain morphology prediction, which is vital for understanding neurodevelopment and diagnosing disorders.

This work introduces several innovations: 1) a UV-based bidirectional mapping reducing permutation issues; 2) a hybrid CNN-Transformer U-Net architecture capturing multi-scale features; 3) a combined loss function integrating spatial and perceptual metrics; 4) application to high-res brain point clouds with over 40,000 points. These innovations enable detailed, global-aware predictions, surpassing prior methods that either focus on local features or global context alone.

• �� Map 3D brain point clouds to 2D UV space using a bi-directional algorithm, handling non-unique points via sorting and nearest-neighbor mapping. • Use residual CNN blocks to extract local features from UV images. • Incorporate transformer modules with multi-head self-attention to model global dependencies, avoiding positional embeddings for robustness. • Employ a U-Net encoder-decoder with skip connections for multi-scale feature fusion. • Design a composite loss function combining L2, TV, latent feature, and perceptual (LPIPS) losses, weighted to balance detail and smoothness. • Apply data augmentation (rotation, flipping, noise) for robustness. • Train with cross-validation to ensure generalization.

Using finite element model-generated datasets with 40,401 surface points and 2,382 fibers, the model was trained to predict cortical folding from early to late gestational stages. Baselines included PointNet and PointNet++, evaluated via Chamfer distance, with hyperparameters tuned for optimal performance. Ablation studies confirmed the importance of UV mapping and transformer modules. The model's robustness was tested across developmental stages, demonstrating consistent accuracy and detailed surface reconstruction.

Trans-Unet achieved a Chamfer distance of 0.015, outperforming PointNet++ (0.025). It accurately modeled cortical folding progression, with errors evenly distributed across stages. Ablation experiments showed that UV mapping and transformer components contributed most to performance gains. The model effectively handled non-unique points and long-range dependencies, demonstrating high fidelity in high-resolution brain surface prediction.

The method can be used for early detection of neurodevelopmental disorders, personalized brain modeling, and simulation of brain growth. It requires high-res point cloud data, which can be obtained from finite element models or advanced imaging. The approach offers a scalable, detailed tool for neuroscientists and clinicians, facilitating better understanding and diagnosis of brain development abnormalities.

Current limitations include sensitivity to occlusions and extreme deformations, high computational demands, and limited validation on diverse clinical datasets. The UV mapping process may introduce artifacts in regions with severe occlusion. Future work should focus on optimizing efficiency, extending validation, and integrating multi-modal data to improve robustness.