Finite Element (FE)-based mechanical models can simulate the brain growth and folding process. But they are time consuming due to the large number of nodes in a real human brain and the reverse process to the initial smooth brain surfaces is difficult because it is not invertible problem. Here, we demonstrate a proof-of-concept that deep-learning neural networks (DNN) can learn the growth and folding process of human brain in forward and reverse directions and can predict/retrieve the developed/primary folding patterns in a very fast speed.
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