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.

翻译：评估癌症患者纵向CT扫描中的病灶演化对于治疗反应评估至关重要，然而在不同时间点间建立可靠的病灶对应关系仍具挑战性。依赖几何邻近性的标准二分匹配方法在病灶出现、消失、合并或分裂时难以有效处理。我们提出一种基于不平衡最优传输的配准感知匹配器，该方法能适应不相等的病灶质量，并使先验适应患者层面的肿瘤负荷变化。我们的传输成本融合了：(i) 尺寸归一化的几何特征，(ii) 来自变形场雅可比行列式的局部配准可信度，以及 (iii) 可选的图像块级外观一致性。通过相对剪枝对所得传输方案进行稀疏化处理，无需重新训练或启发式规则即可生成一对一匹配，以及新出现、消失、合并和分裂的病灶。在纵向CT数据上的实验表明，相较于仅使用距离的基线方法，我们的方法在边缘检测精度与召回率、病灶状态召回率以及病灶图分量F1分数方面均取得持续更优的结果。