Adaptive Multiphase Liver Tumor Segmentation With Multiscale Supervision

The segmentation of liver tumors using multi-phase computed tomography (CT) images has garnered considerable attention in medical signal processing. However, existing multi-phase liver tumor segmentation methods primarily concentrate on feature integration across various phases, neglecting a comprehensive exploration of synergistic relationships among these phases and constraints on features across different scales. This limitation has led to performance bottlenecks in existing approaches. This article proposes a robust multi-phase liver tumor segmentation framework designed to address the aforementioned challenges. Specifically, we introduce a novel multi-phase and channel-stacked dual attention module, seamlessly integrated within a multi-scale architecture. This module adaptively captures essential semantic information among different phases, enhancing the segmentation network's feature extraction capabilities. A scale-weighted loss function for multi-scale supervision is also designed to mitigate false positives in the segmentation results. To facilitate a systematic evaluation of our model's performance on multi-phase data, we curate a new dataset comprising samples from four distinct phases. Our proposed framework is rigorously assessed through comprehensive quantitative and qualitative experiments, highlighting its compelling performance.