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MRGazer:从功能磁共振成像中解码眼睛的凝视点,在个体空间中进行

Xiuwen Wu¹, Rongjie Hu¹, Jie Liang¹

¹Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, People's Republic of China.

Journal of neural engineering

|July 10, 2024

概括

根据fMRI数据,MRGazer可以有效地预测眼睛的目光. 这种深度学习框架简化了处理,提高了认知研究的准确性,超过了以前的方法.

科学领域:

神经科学是一个神经科学.
认知科学认知科学
机器学习机器学习

背景情况:

眼睛跟踪对于理解认知至关重要.
以前用于fMRI眼动分析的深度学习方法需要复杂的共同注册.
这种复杂性导致了处理时间的增加和对外部模板的依赖.

研究的目的:

开发一个有效的框架,从功能磁共振成像 (fMRI) 数据预测眼睛的目光在个人空间.
为了简化fMRI眼动分析的处理协议.
为了实现端到端的眼睛凝视回归,而无需fMRI联合注册.

主要方法:

提出了MRGazer框架,其中包括一个眼球提取模块和一个基于残余网络的眼睛凝视预测模块.
跳过了fMRI联合注册步骤,简化了协议.
实现了端到端的眼睛凝视回归.

主要成果:

与基于联合注册的方法 (EE = 2.89°) 相比,MRGazer实现了优越的眼睛固定回归性能 (欧几里德误差,EE = 2.04°).
该框架的结果显著更快 (∼0.02秒体积−1) 比之前的方法 (∼0.3秒体积−1).

结论:

MRGazer是一个高效,简单和准确的深度学习框架,用于从fMRI数据中预测眼睛运动.
该框架可以在心理和认知研究中的fMRI扫描中使用.
该MRGazer代码是公开可用于研究使用.

关键词:

深层卷积神经网络是一个深层卷积神经网络.数字形态操作操作数字形态操作眼睛跟踪眼睛跟踪对fMRI分析进行分析.对象检测检测对象检测对象检测

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