Preprocessing strategies and their influence on deep learning-driven MRI segmentation

Abstract

In this work, a comprehensive analysis of the impact of intensity value regularization methods on 3D MRI segmentation for three neurological disorders: glioblastoma, multiple sclerosis, and epilepsy, is presented. The experiments were conducted through three architectures: nnU-Net (convolutional neural network), WNet (hybrid combining convolutional and transformer elements), and Primus (transformer-based), considering both FLAIR and T1-weighted images, as well as FLAIR-only scenarios. The statistical analysis conducted underscores the crucial role of intensity regularization in the performance. The results indicate that among the intensity regularization methods tested in this study, KDE, White-stripe, and Z-score standardizations proved to be particularly effective. Furthermore, nnU-Net is the most robust architecture against intensity variability, with small improvements of around 3%. Meanwhile, methods incorporating TF elements are more sensitive to these variations. WNet demonstrates slightly greater gains, around 6%. While Primus can be less stable and underperform compared to nnU-Net and WNet in most cases; nonetheless, it remains a promising and competitive option. Additionally, it has been demonstrated that adding an extra channel does not necessarily guarantee improved performance, while also increasing computational cost.