JParc：联合皮层表面分割与配准
JParc: Joint cortical surface parcellation with registration
AI总结：
JParc通过联合皮层配准与分割方法，利用浅层子网络优化图谱标签，实现高精度的脑皮层分割。
摘要：
皮层表面分割是基础神经科学研究和临床应用中的基本任务，能够更准确地映射大脑区域。基于模型和学习的方法用于自动分割，减轻了手动标注的需求。尽管分割性能有所提升，但基于学习的方法却远离配准和图谱传播，没有探索其比传统方法改进的原因。在本研究中，我们提出了JParc，一个联合皮层配准和分割框架，其性能优于现有最先进的分割方法。在严格实验中，我们证明JParc的性能提升主要归因于准确的皮层配准和学习的分割图谱。通过利用浅层子网络对传播的图谱标签进行微调，JParc在Mindboggle数据集上实现了超过90%的Dice分数，仅使用基本几何特征（裂隙深度、曲率）来描述皮层折叠模式。JParc的高精度可以显著增加脑图谱研究的统计效力，并支持手术规划和其他下游神经科学和临床任务。
英文摘要：
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.
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