PARF-Net: Phase-adaptive robust fusion for liver tumor segmentation with missing-phase CECT

Le résumé fourni par la source

Multi-phase contrast-enhanced CT (CECT) provides complementary diagnostic information for liver tumor segmentation. However, in real clinical practice, random phase missingness makes it difficult for models relying on fixed phase combinations to stably capture multi-source information, thereby limiting their reliability in practical applications. To address this issue, we propose the Phase-Adaptive Robust Fusion Network (PARF-Net) , a unified framework for robust segmentation under single-phase-missing settings. The proposed method integrates a Swin Transformer-based global branch with multi-branch CNNs to jointly model global semantic and local structural features. A phase-adaptive routing mechanism dynamically adjusts fusion weights based on phase availability and feature quality, while local-global feature interaction and consistency regularization enable stable cross-phase fusion and robust representation learning. Extensive experiments on the MPLL and PLC-CECT datasets demonstrate that PARF-Net achieves superior performance under both complete and missing-phase settings. In particular, under missing-phase settings, the proposed method maintains an average Dice score of over 72.5% . In the clinically critical portal venous phase-missing scenario, it achieves a performance gain of more than 6.0% , while providing consistent and reliable predictions across various phase combinations. These results suggest that PARF-Net can provide stable liver tumor segmentation support in real clinical scenarios and improve the clinical applicability of automated segmentation systems for incomplete multi-phase CECT data.

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