Overlap-aware segmentation for topological reconstruction of obscured objects
This paper introduces OASIS, a novel segmentation-regression framework that employs a weighted loss function to prioritize overlapping regions during training, significantly improving the intensity and topological reconstruction of faint, obscured electron tracks in the challenging context of the MIGDAL experiment.