用不平衡最优传输解决肿瘤随时间变化的匹配难题
Unbalanced optimal transport for robust longitudinal lesion evolution with registration-aware and appearance-guided priors
- 基于不平衡最优传输，融合几何、形变可信度和外观一致性
- 在纵向CT中实现更精准的病灶匹配，包括新出现、消失、合并与分裂
- 适用于癌症治疗评估，尤其适合病灶动态变化明显的场景
评估癌症患者纵向CT扫描中的病灶演化对判断治疗反应至关重要，但跨时间建立可靠病灶对应关系仍具挑战。标准双分图匹配器依赖几何邻近性，在病灶出现、消失、融合或分裂时表现不佳。本文提出一种注册感知的不平衡最优传输（UOT）匹配方法，可处理病灶质量不均，并适应患者水平肿瘤负荷变化。运输成本结合了(1)尺寸归一化几何特征，(2)来自形变场雅可比行列式的局部注册可信度，以及(3)可选的局部图像块外观一致性。通过相对剪枝策略稀疏化运输计划，实现一对一匹配及新发、消失、合并与分裂病灶的自动识别，无需重新训练或启发式规则。在纵向CT数据上，本方法在边缘检测的精确率与召回率、病灶状态召回率及病灶图组件F1分数方面均显著优于仅依赖距离的基线模型。
原文摘要 · Abstract (English)
Evaluating lesion evolution in longitudinal CT scans of can cer patients is essential for assessing treatment response, yet establishing reliable lesion correspondence across time remains challenging. Standard bipartite matchers, which rely on geometric proximity, struggle when lesions appear, disappear, merge, or split. We propose a registration-aware matcher based on unbalanced optimal transport (UOT) that accommodates unequal lesion mass and adapts priors to patient-level tumor-load changes. Our transport cost blends (i) size-normalized geometry, (ii) local registration trust from the deformation-field Jacobian, and (iii) optional patch-level appearance consistency. The resulting transport plan is sparsified by relative pruning, yielding one-to-one matches as well as new, disappearing, merging, and splitting lesions without retraining or heuristic rules. On longitudinal CT data, our approach achieves consistently higher edge-detection precision and recall, improved lesion-state recall, and superior lesion-graph component F1 scores versus distance-only baselines.
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