REC-UNet: a 2D U-Net model with residual cross-dimensional attention for liver tumor segmentation

Abstract

Liver tumors impose a significant global health burden, underscoring the urgent need for efficient and accurate diagnostic methods. Computer-assisted techniques, particularly deep learning-based segmentation models, have shown considerable promise in this domain. However, they continue to face persistent challenges in liver tumor segmentation, including severe background noise interference, indistinct lesion boundaries, and the difficulty of simultaneously improving segmentation accuracy while maintaining a balanced trade-off between Recall and Precision. To address these issues, this article proposes a novel residual “Enhancement-Calibration” U-Net architecture, termed REC-UNet. The model consists of two task-specific modules: a Residual Enhancement Module (REM) and a Calibration Module (CM). REM leverages residual connections and cross-dimensional attention to enhance tumor feature representation for accurate segmentation, thereby establishing a foundation for balancing Recall and Precision. CM further mitigates noise propagation from shallow to deep feature layers, refining segmentation precision while sustaining high levels of both Recall and Precision. Experiments on the LiTS2017 and MSD_Task08 liver tumor datasets demonstrate that REC-UNet achieves superior performance over mainstream models, with a 4.34% improvement in Dice and a 4.24% improvement in Intersection over Union (IoU) over the second-best model (VM-UNet) on LiTS2017. We further validate the model on an in-house clinical liver tumor Magnetic Resonance Imaging (MRI) dataset, where it attains a Dice score of 87.88% and an IoU of 86.91%, while maintaining a well-balanced trade-off between Recall and Precision. Importantly, REC-UNet achieves high overall segmentation accuracy across diverse lesion sizes and contrast conditions without relying on explicit size-stratified optimization. These results confirm the robust generalizability of REC-UNet and highlight its significant clinical value for computer-assisted liver tumor diagnosis.