Table 2.

Overview of the different investigated registration algorithms.

	“Default Rigid” (DR)	“Applicator ROI” (AROI)	“Applicator Mask” (AM)	“Distance Map” (DM)	“Prediction” (DM*)
Implementation	Python/ITK
Loss function	Mutual Information (MI)	MI	Kappa Statistic (KS)	MI	MI
Transform	3D Translation + Rotation
Image domain	MRI	MRI	Binary applicator mask	Euclidian distance map	Euclidian distance map
Description	Use full MRI volumes, and default settings. An out-of-the box solution that serves as a benchmark.	Use applicator masks to define valid sampling regions in MRIs.	Register binary applicator masks directly.	Transform binary mask to Euclidian distance maps and register the two distance maps.	Same as “Distance Map” but using the predicted applicator masks from the neural network.