| dc.description.abstract | The purpose of this dissertation is to design a network architecture based on deep learning to reconstruct and deform the 3D medical protector. There are three different types of protector to target the de Quervain Syndrome, carpal tunnel syndrome for hands and insoles for correcting feet, respectively. Usually, traditional methods in protector production are that designers draw the protectors manually, which spend a lot of time. Hence, we train an AutoEncoder network to make the 3D medical protector be reconstructed automatically and satisfy the size of the input data. The costs of time and labor can be reduced; meanwhile, the goal with effectivity and accuracy can be achieved finally for producing the 3D medical protector.
Firstly, we use 3D scanner to collect the data of my hands and feet as training data, after then, the corresponding protector is built manually and it will be regarded as the training ground truth in this study. The points of the training data and ground truth are sampled uniformly, and then, inputting them to the AutoEncoder deep net architecture. The network will learn the main features of latent code during the encoding and decoding processes. As the training steps increase, the results of the decoder reconstruction will be closer and closer to the ground truth. When the training is completed, the trained weights will be saved. In addition, we zoomed and rotated the 3D scan data of my hands and feet as verification data, then, the verification data is inputted to the trained AutoEncoder network as well. The network will reconstruct the 3D medical protector which can satisfy the size of verification data. To quantitatively evaluate performances of the experimental results, we apply MMD-CD and JSD verification metric to verify. Consequently, the suitable 3D medical protector is printed by 3D printer.
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