Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
This addresses the need for more accurate and robust liver segmentation for clinical workflows in cirrhosis patients, though it appears incremental with a 2% improvement.
The paper tackled the problem of volumetric cirrhotic liver segmentation in MRIs by proposing nnSynergyNet3D, a novel synergistic deep learning model that integrates continuous and discrete latent spaces; it outperformed nnUNet3D by approximately 2% on a private MRI dataset and demonstrated superior cross-modal generalization on CT scans.
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.