Diagnosing Crohn's Disease (CD) typically involves examining 2D slices from magnetic resonance enterography (MRE). However, the anisotropic resolution of MRE complicates precise 3D measurements and visualization. The absence of automated 3D measurement systems further complicates assessment. Previous methods for generating isotropic volumes from anisotropic data often rely on extensive 3D data and focus solely on interslice resolution, leading to suboptimal outcomes due to data scarcity and inaccuracies. We propose a self-supervised multi-plane generative model using Generative Adversarial Network (GAN) architecture, incorporating multiple discriminators for different planes. We introduce a semi-automatic algorithm to predict the centerline of the terminal ileum, which is the part of the body primarily affected by CD, enhancing efficiency in 3D MRE analysis. Evaluation using 115 2D abdominal MRE datasets from Rambam Health Care Campus underscores the potential of our approach to enhance diagnostic accuracy and visualization in CD. Our semi-automatic centerline prediction reduces the radiologist's analysis time significantly. Additionally, our isotropic volume generation model can be expanded to other anatomical regions, thereby reducing MRI scan-time and costs.
