JParc: Joint cortical surface parcellation with registration

2025-12-27

AI Summary

Existing learning-based cortical surface parcellation methods lack in-depth analysis of performance improvement mechanisms—particularly their interplay with registration and atlas propagation. Method: We propose the first end-to-end joint cortical registration and parcellation framework, featuring deep coupling between the two tasks: a learnable atlas propagation module and a shallow fine-tuning subnetwork; a lightweight geometric-feature-driven architecture (using sulcal depth and curvature) that jointly optimizes diffeomorphic registration and label propagation. Results: On the Mindboggle dataset, our method achieves Dice scores exceeding 90%, significantly outperforming both conventional and state-of-the-art learning-based approaches. Ablation studies confirm registration quality as the key bottleneck limiting parcellation accuracy. Our framework enhances anatomical consistency and label fidelity of cortical parcellations while improving statistical power of brain atlases—thereby providing robust support for clinical applications such as neurosurgical planning.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Segmentation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Cortical surface parcellation is a fundamental task in both basic neuroscience research and clinical applications, enabling more accurate mapping of brain regions. Model-based and learning-based approaches for automated parcellation alleviate the need for manual labeling. Despite the advancement in parcellation performance, learning-based methods shift away from registration and atlas propagation without exploring the reason for the improvement compared to traditional methods. In this study, we present JParc, a joint cortical registration and parcellation framework, that outperforms existing state-of-the-art parcellation methods. In rigorous experiments, we demonstrate that the enhanced performance of JParc is primarily attributable to accurate cortical registration and a learned parcellation atlas. By leveraging a shallow subnetwork to fine-tune the propagated atlas labels, JParc achieves a Dice score greater than 90% on the Mindboggle dataset, using only basic geometric features (sulcal depth, curvature) that describe cortical folding patterns. The superior accuracy of JParc can significantly increase the statistical power in brain mapping studies as well as support applications in surgical planning and many other downstream neuroscientific and clinical tasks.

Problem

• Joint cortical registration and parcellation framework
• Outperforms existing state-of-the-art parcellation methods
• Enhances brain mapping accuracy for clinical applications

Innovation

• Joint cortical registration and parcellation framework
• Fine-tunes propagated atlas labels via shallow subnetwork
• Uses basic geometric features like sulcal depth