Are Learned Vascular Tree Generators Better Than Conditional Resampling?

Overview & Synopsis

Fidelity scores compare generated vascular trees with held-out real trees, so by themselves they cannot detect reuse of training data. A training-free conditional resampler shows this. On coronary trees (ImageCAS) and on IntrA, about half of its branch records (branches with their parent context) are near-copies of training records. Yet on our two fidelity scores, 1-NNA on tree summaries and a parent-daughter dependence AUC, it has better point estimates than three learned baselines as retrained here. A training copy scores better still. Adding noise to the copied values traces a curve of fidelity against the measured branch-record near-copy rate. At their own coronary rates (at most 1.3%), two learned baselines lie above this curve (worse) on parent-daughter relations. VesselGPT is inconclusive against the curve, as it is against the resampler on IntrA and on scale-free tree features. The curve is a reference, not a bound: real validation trees lie below it on both coronary scores, so low near-copy rates are not a goal in themselves. Jittered training copies also lie below it. On coronary data, one with a near-copy rate of about 1% does so on the dependence AUC, and so does one whose stronger jitter ruins its tree summaries. On IntrA, a lightly jittered copy lies below it on 1-NNA. A matched near-copy rate therefore certifies neither score against perturbed copies. The study is exploratory. We offer the near-copy rate as a diagnostic, reported beside fidelity with three controls: a training copy, a jittered copy and real held-out trees.