DynoDINO：利用DINO特征中的动态潜在信息进行多阶段医学图像分割
DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation
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中文总结 AI 辅助
DynoDINO是统一多阶段医学图像分割框架，通过切片级对齐、含混合注意力与自适应门控的多阶段融合模型，在三个数据集上提升了多阶段医学图像分割的边界与结构保真度。
中文摘要 AI 辅助
多期增强计算机断层扫描（CECT）通过捕获多个采集阶段的时间增强模式，在局灶性病变的诊断与特征表征中发挥核心作用。从这类数据中准确分割病变仍具挑战性，因为临床相关的对比动力学分布在不同阶段，而解剖结构不一致、呼吸运动及不完整采集常导致阶段间错位和时间信息中断。传统分割框架通常独立处理每个阶段或依赖简单融合策略，限制了时间推理能力。为应对这些挑战，我们提出DynoDINO，一个专为解决多阶段医学图像分割核心挑战设计的统一框架。DynoDINO首先执行切片级对齐以建立阶段间解剖对应关系，随后采用多阶段融合模型共同增强阶段间的时间相关性。该融合模型包含用于高效多阶段特征校准的混合注意力（MA）机制，以及结合基于差异的残差学习的自适应门控机制，可选择性保留诊断相关的对比变化，同时抑制由残余错位导致的伪影。此外，自适应门控机制通过防止无引导减法操作引发的特征退化，提升了训练稳定性。在包括LiTS、PLC-CECT和WAW-TACE在内的三个大规模数据集上的实验表明，DynoDINO在标准、移位及缺失阶段条件下，均能持续改善边界描绘和结构保真度。
英文摘要
Multi-phase Contrast-Enhanced Computed Tomography (CECT) plays a central role in the diagnosis and characterization of focal lesions by capturing temporal enhancement patterns across multiple acquisition phases. Accurate lesion segmentation from such data remains challenging because clinically relevant contrast kinetics are distributed across phases, while anatomical inconsistencies, respiratory motion, and incomplete acquisitions often lead to inter-phase misalignment and interrupted temporal information. Conventional segmentation frameworks typically process each phase independently or rely on simple fusion strategies, limiting their temporal reasoning capability. To address these challenges, we propose DynoDINO, a unified framework tailored to address the core challenges of multi-phase medical image segmentation. DynoDINO first performs slice-level alignment to establish inter-phase anatomical correspondence and then employs a Multi-phase Fusion Model to jointly enhance temporal correlations across phases. Our fusion model incorporates a Mix-attention (MA) mechanism for efficient multi-phase feature calibration and an Adaptive Gating Mechanism with difference-based residual learning to selectively preserve diagnostically relevant contrast variations while suppressing artifacts caused by residual misalignment. In addition, the adaptive gating mechanism improves training stability by preventing feature degradation caused by unguided subtraction operations. Experiments on three large-scale datasets, including LiTS, PLC-CECT, and WAW-TACE, demonstrate that DynoDINO consistently improves boundary delineation and structural fidelity under standard, shifted, and missing-phase conditions.
