Unbalanced optimal transport for robust longitudinal lesion evolution with registration-aware and appearance-guided priors
This addresses the challenge of assessing treatment response in cancer patients by improving lesion evolution tracking, though it is an incremental advance in medical imaging analysis.
The paper tackled the problem of reliably matching lesions across longitudinal CT scans in cancer patients, where lesions can appear, disappear, merge, or split, by proposing a registration-aware matcher based on unbalanced optimal transport. It achieved consistently higher edge-detection precision and recall, improved lesion-state recall, and superior lesion-graph component F1 scores compared to distance-only baselines.
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.