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<p>Accurate brain parcellation from structural MRI across the human lifespan is essential for advancing neuroimaging and neuroscience studies. However, existing methods often struggle to generalize owing to intensity and contrast variations across brain maturation, aging and differences in MRI acquisition protocols, limiting their clinical and research utility. Here we present BrainParc, a unified parcellation framework that leverages anatomical information invariant to intensity and contrast, enabling accurate, robust and longitudinally consistent parcellation across a heterogeneous dataset without the need for fine-tuning. Extensive experiments on both internal and external datasets demonstrate that BrainParc substantially outperforms state-of-the-art methods in delineating 106 brain regions. BrainParc consistently shows better performance across diverse populations and imaging conditions, both quantitatively and qualitatively. Beyond anatomical segmentation, we show that BrainParc enables reliable tracking of brain development and facilitates early diagnosis of neurological disorders, underscoring its potential as a robust and generalizable tool for large-scale neuroimaging studies and clinical translation.</p>

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BrainParc: unified lifespan brain parcellation from structural magnetic resonance images

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Jiameng Liu,
  • Feihong Liu,
  • Kaicong Sun,
  • Zhiming Cui,
  • Tianyang Sun,
  • Zehong Cao,
  • Jiawei Huang,
  • Shuwei Bai,
  • Yulin Wang,
  • Yulong Dou,
  • Kaicheng Zhang,
  • Caiwen Jiang,
  • Yuyan Ge,
  • Han Zhang,
  • Feng Shi,
  • Dinggang Shen

摘要

Accurate brain parcellation from structural MRI across the human lifespan is essential for advancing neuroimaging and neuroscience studies. However, existing methods often struggle to generalize owing to intensity and contrast variations across brain maturation, aging and differences in MRI acquisition protocols, limiting their clinical and research utility. Here we present BrainParc, a unified parcellation framework that leverages anatomical information invariant to intensity and contrast, enabling accurate, robust and longitudinally consistent parcellation across a heterogeneous dataset without the need for fine-tuning. Extensive experiments on both internal and external datasets demonstrate that BrainParc substantially outperforms state-of-the-art methods in delineating 106 brain regions. BrainParc consistently shows better performance across diverse populations and imaging conditions, both quantitatively and qualitatively. Beyond anatomical segmentation, we show that BrainParc enables reliable tracking of brain development and facilitates early diagnosis of neurological disorders, underscoring its potential as a robust and generalizable tool for large-scale neuroimaging studies and clinical translation.