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Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs

Vandan Gorade, Onkar Susladkar, Gorkem Durak, Elif Keles, Ertugrul Aktas, Timurhan Cebeci, Alpay Medetalibeyoglu, Daniela Ladner, Debesh Jha, Ulas Bagci

TL;DR

The paper tackles the challenge of accurate and robust cirrhotic liver segmentation in MRI by addressing limitations in capturing complex feature interactions and cross-dataset generalization. It introduces a synergistic latent-space theory that combines continuous and discrete feature representations, implemented in the nnSynergyNet3D architecture with a 3D cascade U-Net, volumetric vector quantization, and cross-attention, along with auto-configuration. Empirically, the method outperforms state-of-the-art baselines on a private MRI dataset by about 2% and demonstrates superior cross-modal generalization in zero-shot tests on LiTS CT data, suggesting better consistency across MRI and CT modalities. These results point to meaningful improvements in segmentation accuracy and robustness, with potential to streamline clinical workflows and extend synergistic representations to broader medical imaging tasks.

Abstract

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.

Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs

TL;DR

The paper tackles the challenge of accurate and robust cirrhotic liver segmentation in MRI by addressing limitations in capturing complex feature interactions and cross-dataset generalization. It introduces a synergistic latent-space theory that combines continuous and discrete feature representations, implemented in the nnSynergyNet3D architecture with a 3D cascade U-Net, volumetric vector quantization, and cross-attention, along with auto-configuration. Empirically, the method outperforms state-of-the-art baselines on a private MRI dataset by about 2% and demonstrates superior cross-modal generalization in zero-shot tests on LiTS CT data, suggesting better consistency across MRI and CT modalities. These results point to meaningful improvements in segmentation accuracy and robustness, with potential to streamline clinical workflows and extend synergistic representations to broader medical imaging tasks.

Abstract

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.
Paper Structure (10 sections, 19 equations, 2 figures, 3 tables)

This paper contains 10 sections, 19 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Problem Statement
  4. Synergistic Latent Space Theory
  5. Practical Architecture Design
  6. Experimental Setup
  7. Empirical Analysis
  8. Conclusion
  9. Appendix
  10. Detailed Proofs

Figures (2)

  • Figure 1: Visual Representation of Proposed Synergistic Latent Space Methodology: In the diagram, $f_1$ and $f_2$ represent transformation functions that map one feature space to another. The circular arrows indicate auto-configuration processes. $F_1$ and $F_2$ denote complementary feature spaces, while $S$ represents the synergistic latent space that integrates and enhances the information from $F_1$ and $F_2$.
  • Figure 2: Qualitative comparison between nnsynergyNet3D and nnUNet on segmenting mild and severe cirrhosis from abdominal T1-weighted and T2-weighted MRI scans. The yellow bounding circles show major mistakes made by the networks.