Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution

Abstract

In medical imaging, the lack of high-quality images is present in many areas such as magnetic resonance (MR). Due to many acquisition impediments, the generated images have not enough resolution to carry out an adequate diagnosis. Image super-resolution (SR) is an ill-posed problem that tries to infer information from the image to enhance its resolution. Nowadays, deep learning techniques have become a powerful tool to extract features from images and infer new information. In MR, most of the recent works are based on the minimization of the errors between the input and the output images based on the Euclidean norm. This work presents a new methodology to perform three-dimensional SR based on the combination of Lp-norms in the loss layer. Two multiobjective optimization techniques are used to combine two cost functions. The proposed loss layers were trained with the SRCNN3D and DCSRN networks and tested with two MR structural T1-weighted datasets, and then compared with the traditional Euclidean loss. Experimental results show significant differences in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Bhattacharyya Coefficient (BC), while the residual images show refined details.