Machine Learning Matches Human Performance at Segmenting the Human Visual Cortex.

ABSTRACT: A major problem in human visual neuroscience research is the localization of visual areas on the cortical surface. Currently available methods are capable of making detailed predictions about many areas, but human raters do not agree as well with these methods as they do with each other. Although highly accurate, human raters require substantial time and expertise that many researchers do not have. Additionally, human raters require functional data for drawing visual area boundaries that requires additional scan time, budget, and expertise to collect. Here, we train convolutional neural network (CNN) models to predict the boundaries and iso-eccentric regions of V1, V2, and V3 in both the Human Connectome Project dataset and the NYU Retinotopy dataset. CNNs trained to use the same functiona

SUBMITTER: Benson NC 

PROVIDER: S-EPMC12139749 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature