  Liver cirrhosis, a leading cause of global mortality, requires precise
segmentation of ROIs for effective disease monitoring and treatment planning.
Existing segmentation models often fail to capture complex feature interactions
and generalize across diverse datasets. To address these limitations, we
propose a novel synergistic theory that leverages complementary latent spaces
for enhanced feature interaction modeling. Our proposed architecture,
nnSynergyNet3D integrates continuous and discrete latent spaces for 3D volumes
and features auto-configured training. This approach captures both fine-grained
and coarse features, enabling effective modeling of intricate feature
interactions. We empirically validated nnSynergyNet3D on a private dataset of
628 high-resolution T1 abdominal MRI scans from 339 patients. Our model
outperformed the baseline nnUNet3D by approximately 2%. Additionally, zero-shot
testing on healthy liver CT scans from the public LiTS dataset demonstrated
superior cross-modal generalization capabilities. These results highlight the
potential of synergistic latent space models to improve segmentation accuracy
and robustness, thereby enhancing clinical workflows by ensuring consistency
across CT and MRI modalities.
