Table 7.

DL-based methods using the 2017 AAPM Thoracic Auto-segmentation Challenge datasets.

Metric	Method	Esophagus	Heart	Left Lung	Right Lung	Spinal Cord
DSC	DCNN Team Elekta*	0.72±0.10	0.93±0.02	0.97±0.02	0.97±0.02	0.88±0.037
	3D U-Net [265]	0.72±0.10	0.93±0.02	0.97±0.02	0.97±0.02	0.89±0.04
	Multi-class CNN Team Mirada*	0.71±0.12	0.91±0.02	0.98±0.02	0.97±0.02	0.87±0.110
	2D ResNet Team Beaumont*	0.61±0.11	0.92±0.02	0.96±0.03	0.95±0.05	0.85±0.035
	3D and 2D U-Net Team WUSTL*	0.55±0.20	0.85±0.04	0.95±0.03	0.96±0.02	0.83±0.080
	U-Net-GAN [45]	0.75±0.08	0.87±0.05	0.97±0.01	0.97±0.01	0.90±0.04
MSD (mm)	DCNN Team Elekta*	2.23±2.82	2.05±0.62	0.74±0.31	1.08±0.54	0.73±0.21
	3D U-Net [265]	2.34±2.38	2.30±0.49	0.59±0.29	0.93±0.57	0.66±0.25
	Multi-class CNN Team Mirada*	2.08±1.94	2.98±0.93	0.62±0.35	0.91±0.52	0.76±0.60
	2D ResNet Team Beaumont*	2.48±1.15	2.61±0.69	2.90±6.94	2.70±4.84	1.03±0.84
	3D and 2D U-Net Team WUSTL*	13.10±10.39	4.55±1.59	1.22±0.61	1.13±0.49	2.10±2.49
	U-Net-GAN [45]	1.05±0.66	1.49±0.85	0.61±0.73	0.65±0.53	0.38±0.27
HD95 (mm)	DCNN Team Elekta*	7.3+10.31	5.8±1.98	2.9±1.32	4.7±2.50	2.0±0.37
	3D U-Net [265]	8.71+10.59	6.57±1.50	2.10±0.94	3.96±2.85	1.89±0.63
	Multi-class CNN Team Mirada*	7.8±8.17	9.0±4.29	2.3±1.30	3.7±2.08	2.0±1.15
	2D ResNet Team Beaumont*	8.0±3.80	8.8±5.31	7.8±19.13	14.5±34.4	2.3±0.50
	3D and 2D U-Net Team WUSTL*	37.0±26.88	13.8±5.49	4.4±3.41	4.1±2.11	8.10±10.72
	U-Net-GAN [45]	4.52±3.81	4.58±3.67	2.07±1.93	2.50±3.34	1.19±0.46

^*Note: Participating methods of the AAPM thorax challenge [263].