TABLE I:

Segmentation results on three datasets when full (100%) or zero (0%) target modality annotations are available. For each dataset we show results on the target modality assuming the other is the source (and vice versa). Single input-output models cannot be trained with no annotations and are marked with n/a. We omit results marked with –, since training of these methods did not converge.

				100% target annotations
Methods	Train	Test	Masks in test	ERI		BOLD		CHAOS
				LGE	cine	BOLD	cine	T2	T1
copy	–	multi	Yes	6706	6706	8001	8001	7110	7110
register	–	multi	Yes	6807	6705	8104	8405	7007	7305
AC	–	multi	Yes	6615	6613	6802	7205	6522	6522
UNet	single	single	No	7804	8508	9101	8901	8517	8605
SDNet	single	single	No	8003	8409	8903	8804	8316	8508
UNet	multi	single	No	8103	8308	8903	8802	8515	8803
SDNet	multi	single	No	8005	8605	8902	8703	8511	8801
DualStream	multi	single	No	8006	8609	8909	8802	8516	8509
Translation	multi	single	No	7906	8405	8306	8802	8309	8706
DADR	multi	single	No	7905	8306	8804	8602	8416	7222
DAFNet	multi	single	No	8203	8602	8801	9102	8317	8801
DAFNet	multi	multi	No	8203	8402	9101	9101	${\mathbf{85}}_{05}^{\ast }$	8701
				0% target annotations
Methods	Train	Test	Masks in test	ERI		BOLD		CHAOS
				LGE	cine	BOLD	cine	T2	T1
copy	–	multi	Yes	6706	6706	8001	8001	7110	7110
register	–	multi	Yes	6807	6705	8104	8405	7007	7305
AC	n/a	multi	Yes	6615	6613	6802	7205	6522	6522
UNet	single	single	No	n/a	n/a	n/a	n/a	n/a	n/a
SDNet	single	single	No	n/a	n/a	n/a	n/a	n/a	n/a
UNet	multi	single	No	3823	6812	6823	8505	–	–
SDNet	multi	single	No	6118	7307	8003	8503	5109	6313
DualStream	multi	single	No	3823	6812	6823	8505	–	–
Translation	multi	single	No	3723	6113	6110	7407	–	4511
DADR	multi	single	No	4619	6313	6811	8501	–	4917
DAFNet	multi	single	No	7206	7805	7802	8203	7212	7406
DAFNet	multi	multi	No	${\mathbf{74}}_{04}^{\ast }$	7604	${\mathbf{85}}_{03}^{\ast }$	8602	${\mathbf{74}}_{03}^{\ast }$	7106