RT Journal Article
T1 Preprocessing strategies and their influence on deep learning-driven MRI segmentation
A1 Jiménez-Partinen, Ariadna
A1 López-Rubio, Ezequiel
A1 Nagib-Raya, Fátima
A1 Palomo-Ferrer, Esteban José
A1 Luque-Baena, Rafael Marcos
K1 Imágenes por resonancia magnética
K1 Esclerosis múltiple
K1 Epilepsia
AB 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.
PB Elsevier
YR 2026
FD 2026-05
LK https://hdl.handle.net/10630/46031
UL https://hdl.handle.net/10630/46031
LA eng
NO Jiménez-Partinen, Ariadna, López-Rubio, Ezequiel, Nagib-Raya, Fátima, Palomo, Esteban J., Luque-Baena, Rafael M. (2026). Preprocessing strategies and their influence on deep learning-driven MRI segmentation, Pattern Recognition Letters, Volume 203, 2026, Pages 111-118, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2026.02.030
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 mar 2026