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用于体积神经管区分的任意模式融合网络

Lei Xie¹, Huajun Zhou², Junxiong Huang¹

¹Institute of Advanced Technology, Zhejiang University of Technology, Hangzhou, China.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|September 5, 2025

概括

我们开发了 CNTSeg-v2,一种新的 AI 模型,用于使用各种 MRI 数据组合对大脑神经进行细分. 这种方法实现了最高性能,改善了神经通路的分析.

科学领域:

神经成像
医学图像分析
计算神经科学

背景情况:

对神经 (CN) 的准确细分对于其形态和轨迹的定量分析至关重要.
现有的多式联通细分网络 (如CNTSeg) 是有前途的,但需要完整的多式联通数据,这在临床上往往是不可行的.
数据采集,隐私和设备的局限性阻碍了多模式MRI用于CN细分的常规使用.

研究的目的:

提出 CNTSeg-v2,一种用于体积神经细分的新型随机模式融合网络.
开发一种能够处理各种可用的MRI模式的单一模型.
在临床环境中提高CN细分的效率和可行性.

主要方法:

开发了CNTSeg-v2,用于体积CN细分的任意模式融合网络.
使用T1加权 (T1w) 核磁共振作为监测的主要方式,指导从辅助方式中选择特征.
采用任意模式协作模块 (ACM) 进行有效的特征提取,并采用深度距离引导多级解码器 (DDM) 来使用签名距离地图进行错误纠正.

主要成果:

CNTSeg-v2在体积神经细分方面表现出最先进的性能.
与所有竞争方法相比,该模型在人类结合体项目 (HCP) 和多扩散核磁共振 (MDM) 数据集上取得了优异的结果.
随意模式融合方法有效地利用了可用的MRI数据组合,以提高细分精度.

结论:

CNTSeg-v2提供了一种灵活且高性能的解决方案,用于使用多种MRI数据进行骨神经细分.
拟议的方法克服了临床实践中需要完整的多模式数据集的局限性.
CNTSeg-v2代表了大脑神经通道自动化神经成像分析的重大进展.

关键词:

随意的模式融合头骨神经通道的细分扩散核磁共振结构核磁共振

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