Fig. 2.

Summary of major steps in this study. For each patient in our cohort, two brachytherapy fraction treatment plans, and corresponding MRI-volumes and applicator segmentations were exported from the treatment planning system. An auto-segmentation neural network was trained to predict the applicator structure in unseen MR-volumes. Finally, different applicator-based rigid image registration algorithms were compared, initially with ground truth (GT), and eventually with predicted applicator segmentations (AS). The registration accuracy was evaluated by using the distance between dwell positions as a metric. *) For some patients only one fraction was available.