https://www.selleckchem.com/mTOR.html
Medical shapes alignment can provide doctors with abundant structure information of the organs. As for a pair of the given related medical shapes, the traditional registration methods often depend on geometric transformations required for iterative search to align two shapes. To achieve the accurate and fast alignment of 3D medical shapes, we propose an unsupervised and nonrigid registration network. Different from the existing iterative registration methods, our method estimates the point drift for shape alignment directly by learning the displace
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