SKULL STRIPPING USING GENERATIVE ADVERSARIAL NETWORKS WITH POSITION CORRECTION BY POSTURE ESTIMATION

Skull-stripping (SS) from brain magnetic resonance imaging (MRI) data is an essential first step in almost neuroimaging application, automatic diagnosis of Alzheimer’s disease, structure analysis, CBRI system and so on. In this paper, we propose adversarial generative skull stripping method (GASS) for fast, accurate and robust SS. The GASS method learns a limited number of brains ss data and performs fast and accurate SS. In addition, changes in the MRI image due to the posture of the patient during imaging may cause a decrease in the accuracy of SS. To reduce this problem, the GASS method performs SS after applying position correction using posture estimation. GASS achieved the dice index of 96.86% in an evaluation experiment using the ADNI2 dataset of 617 patients. There was not a single case in which the dice index was less than 90%, indicating a high degree of robustness