Tu Yanshuai, Ta Duyan, Lu Zhong-Lin, Wang Yalin
Arizona State University, Tempe AZ 85201, USA.
New York University, New York, NY.
Med Image Comput Comput Assist Interv. 2020;12267:218-227. doi: 10.1007/978-3-030-59728-3_22. Epub 2020 Sep 29.
The mapping between the visual input on the retina to the cortical surface, i.e., retinotopic mapping, is an important topic in vision science and neuroscience. Human retinotopic mapping can be revealed by analyzing cortex functional magnetic resonance imaging (fMRI) signals when the subject is under specific visual stimuli. Conventional methods process, smooth, and analyze the retinotopic mapping based on the parametrization of the (partial) cortical surface. However, the retinotopic maps generated by this approach frequently contradict neuropsychology results. To address this problem, we propose an integrated approach that parameterizes the cortical surface, such that the parametric coordinates linearly relates the visual coordinate. The proposed method helps the smoothing of noisy retinotopic maps and obtains neurophysiological insights in human vision systems. One key element of the approach is the Error-Tolerant Teichmüller Map, which uniforms the angle distortion and maximizes the alignments to self-contradicting landmarks. We validated our overall approach with synthetic and real retinotopic mapping datasets. The experimental results show the proposed approach is superior in accuracy and compatibility. Although we focus on retinotopic mapping, the proposed framework is general and can be applied to process other human sensory maps.
视网膜上的视觉输入与皮质表面之间的映射，即视网膜拓扑映射，是视觉科学和神经科学中的一个重要课题。当受试者受到特定视觉刺激时，通过分析皮质功能磁共振成像（fMRI）信号可以揭示人类视网膜拓扑映射。传统方法基于（部分）皮质表面的参数化来处理、平滑和分析视网膜拓扑映射。然而，这种方法生成的视网膜拓扑图常常与神经心理学结果相矛盾。为了解决这个问题，我们提出了一种综合方法，对皮质表面进行参数化，使参数坐标与视觉坐标线性相关。所提出的方法有助于平滑有噪声的视网膜拓扑图，并获得人类视觉系统的神经生理学见解。该方法的一个关键要素是容错泰希米勒映射，它使角度失真均匀化，并使与自相矛盾的地标对齐最大化。我们用合成和真实的视网膜拓扑映射数据集验证了我们的整体方法。实验结果表明，所提出的方法在准确性和兼容性方面具有优势。虽然我们专注于视网膜拓扑映射，但所提出的框架是通用的，可应用于处理其他人类感觉图谱。
