Performance Measurement
After training, the accuracy of each DIRNet model was evaluated by three different similarity metrics. The trained models were applied to the concatenated pair of fixed and moving images to extract the DVF. Next, the extracted DVF was applied to the moving binary mask to wrap it into the fixed binary mask. As such, each organ could be evaluated separately with pixel-level precision. Each similarity measurement score was averaged over the entire patient slices. For the entire OARs (bladder, cervix, and rectum), three different similarity metrics, including two boundary-based and one surface-based metric, were calculated for each slice separately. Afterward, its average was calculated over the entire 2D slices. The mean ± SD calculated over the 2D slices are reported for each of the abovementioned metrics.