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Abstract #3546

A Self-Regularized and Over-Determined Deep Network for Cranial Pseudo-CT Generation

Max W.K. Law1, Oilei Wong1, Jing Yuen1, and S.K. Yu1
1Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong

This study presented a hyperparameter-free deep network modal for cranial pseudo-CT generation. The model was potentially universal to various scanning machines without the need of network hyperparameter adjustment and could handle testing images from MR- and CT-simulators different from the training data. It is beneficial to perform clinical trial in institutions where multiple MR- and CT-machines are in operations, without supervision by deep learning experts. The proposed model was examined using training and testing datasets acquired from two sets of MR- and CT-simulators, showing promising accuracy, <79 mean-absolute-error and <170 root-mean-squared-error.

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