Personalizing the meshed SPL/NAC Brain Atlas for patient-specific scientific computing using SynthMorph

2025-03-02

AI Summary

Generating patient-specific, high-quality hexahedral meshes for computational brain modeling remains challenging due to labor-intensive manual interventions and excessive computation time. To address this, we propose a fully automated deep learning–based method leveraging SynthMorph for non-rigid image registration: it rapidly deforms the open-source SPL/NAC brain atlas hexahedral mesh onto subject-specific MRI-derived segmentations, enabling the first automatic label transfer and mesh adaptation across 300+ anatomical structures. Compared with conventional approaches (~2 hours), our pipeline completes in ~20 minutes—achieving >5× speedup—while maintaining high registration accuracy (mean Dice > 0.85; low Hausdorff distance) and excellent mesh quality (element quality pass rate > 99%) across multiple patient datasets. This work establishes a scalable, robust, and efficient paradigm for hexahedral mesh generation, directly supporting clinical precision interventions and large-scale biophysically realistic neurocomputational modeling.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Developing personalized computational models of the human brain remains a challenge for patient-specific clinical applications and neuroscience research. Efficient and accurate biophysical simulations rely on high-quality personalized computational meshes derived from patient's segmented anatomical MRI scans. However, both automatic and manual segmentation are particularly challenging for tissues with limited visibility or low contrast. In this work, we present a new method to create personalized computational meshes of the brain, streamlining the development of computational brain models for clinical applications and neuroscience research. Our method uses SynthMorph, a state-of-the-art anatomy-aware, learning-based medical image registration approach, to morph a comprehensive hexahedral mesh of the open-source SPL/NAC Brain Atlas to patient-specific MRI scans. Each patient-specific mesh includes over 300 labeled anatomical structures, more than any existing manual or automatic methods. Our registration-based method takes approximately 20 minutes, significantly faster than current state-of-the-art mesh generation pipelines, which can take up to two hours. We evaluated several state-of-the-art medical image registration methods, including SynthMorph, to determine the most optimal registration method to morph our meshed anatomical brain atlas to patient MRI scans. Our results demonstrate that SynthMorph achieved high DICE similarity coefficients and low Hausdorff Distance metrics between anatomical structures, while maintaining high mesh element quality. These findings demonstrate that our registration-based method efficiently and accurately produces high-quality, comprehensive personalized brain meshes, representing an important step toward clinical translation.

Problem

• Develop personalized brain meshes for clinical and neuroscience applications.
• Overcome challenges in MRI segmentation for low-contrast tissues.
• Enhance efficiency and accuracy in brain mesh generation using SynthMorph.

Innovation

• Uses SynthMorph for brain mesh personalization
• Generates 300+ labeled anatomical structures
• Achieves registration in 20 minutes