快速自动分析大脑胼胝体形态，助力神经疾病研究
FastSurfer-CC: A robust, accurate, and comprehensive framework for corpus callosum morphometry
- 全自动识别中矢状面并分割胼胝体与穹窿
- 生成厚度曲线和8个形态指标，检测出现有方法忽略的疾病差异
- 适合阿尔茨海默病、亨廷顿病等脑部疾病研究者使用
胼胝体是人脑最大的连合结构，在衰老和神经系统疾病研究中备受关注，也是深部脑刺激等干预手段及临床试验（如促进髓鞘再生疗法）的重要生物标志物。尽管已有大量关于胼胝体分割的研究，但公开可用的完整自动化分析工具仍十分有限。为此，我们提出FastSurfer-CC——一种高效、全自动的胼胝体形态测量框架。该框架可自动识别中矢状切片，分割胼胝体与穹窿，定位前、后连合以标准化头位，生成厚度剖面与区域划分，并提取8个形状指标用于统计分析。实验表明，FastSurfer-CC在各项任务中均优于现有专用工具。更重要的是，其能检测出亨廷顿病患者与健康对照组间未被当前最先进方法发现的显著差异。
原文摘要 · Abstract (English)
The corpus callosum, the largest commissural structure in the human brain, is a central focus in research on aging and neurological diseases. It is also a critical target for interventions such as deep brain stimulation and serves as an important biomarker in clinical trials, including those investigating remyelination therapies. Despite extensive research on corpus callosum segmentation, few publicly available tools provide a comprehensive and automated analysis pipeline. To address this gap, we present FastSurfer-CC, an efficient and fully automated framework for corpus callosum morphometry. FastSurfer-CC automatically identifies mid-sagittal slices, segments the corpus callosum and fornix, localizes the anterior and posterior commissures to standardize head positioning, generates thickness profiles and subdivisions, and extracts eight shape metrics for statistical analysis. We demonstrate that FastSurfer-CC outperforms existing specialized tools across the individual tasks. Moreover, our method reveals statistically significant differences between Huntington's disease patients and healthy controls that are not detected by the current state-of-the-art.
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