Assignment strategy (FP2.5.8) - double assignment, one prediction for multiple references.. also covered?

Dear Metric Reloaded Team,

I have worked with Brain metastases data, and I was confused when I evaluated lesion-wise (instance-level) detection metrics (F1 -score) from predicted segmentation. Currently, I match each lesion if there is overlap, or overlap(DSC) > 0.1. And I observed that there are quite multiple assigned cases in both ways (one pred - multiple GTs, multiple pred - one GT). That's how I visit your great work (Metric Reloaded) to get some guidance.

I have briefly checked resolve ambiguity code, but I'm still not sure whether this code also treat the remained GTs as FN or ignore them. Or counting TPs is always based on GTs?

I illustrated this issue with the image in the below repository as well, since I used their code.

https://github.com/rachitsaluja/BraTS-2023-Metrics/issues/11

Start with MetricsReloaded/utility/assignment_localization.py around the referenced resolve ambiguity code at line 510, then compare the question with the linked BraTS-2023-Metrics issue. Determine the intended treatment of unmatched references and predictions and how TP, FP, and FN counts are defined; done requires a clear documented or tested resolution.