UCMUNET Liver

Unified Cross-Modality 3D U-Net to Enhance Liver Segmentation in Cirrhotic Patients

Abstract

Accurate liver segmentation in cirrhotic MRI remains challenging due to intensity variability and morphological deformation across imaging modalities. The CirrMRI600+ dataset provides independent T1-weighted and T2-weighted MRI cohorts, making direct multimodal fusion non-trivial. In this study, we propose a joint 3D U-Net training framework that learns from both T1-weighted (T1) and T2-weighted (T2) Magnetic Resonance Imaging (MRI) modalities using a single shared segmentation head. Unlike modality-specific or multitask approaches, our model is trained on mixed-modality batches to promote modality-invariant representation learning. To achieve stable optimization and precise boundary delineation, we employ a hybrid loss combining Focal Tversky Loss (FTL) and Binary Cross-Entropy (BCE). Experimental results demonstrate that the proposed method outperforms baseline architectures, as well as multitask architectures, achieving a mean Dice of 0.9352 and mean IoU of 0.8801, with Dice scores of 0.9511 and 0.9193 for T1 and T2, respectively. These findings highlight that a well-optimized, modality-general U-Net can achieve robust and accurate liver segmentation in liver cirrhotic MRI without explicit modality-specific adaptation.