Unsupervised Deep Learning Method for Bias Correction

Maria Perez-Caballero, Sergio Morell-Ortega, Marina Ruiz Perez, Pierrick Coupe, Jose V Manjon

Keywords: Unsupervised, MRI, Inhomogenity Correction

Abstract: In this paper, a new method for automatic MR image inhomogeneity correction is proposed. This method, based on deep learning, uses unsupervised learning to estimate the bias corrected images minimizing a cost function based on the entropy of the corrupted image, the derivative of the estimated bias field and corrected image statistics. The proposed method has been compared with the state-of-the-art method N4 providing improved results.

Submission Number: 222