Table 7.

Liver tumor segmentation results of three challenges ranked by segmentation metrics (i.e., Dice, ASD and RVD) and detection metrics (i.e., precision, recall and separated F1 scores with three different sizes of lesion). Each metric value is followed by a ranking score in the bracket. Top performing teams in tumor segmentation and tumor detection are highlighted with blue and pink colors in each metric, respectively. Re-ranking* denotes a post-challenge ranking considering three metrics by averaging the ranking scores.

	Ranking	Ref. Name	Dice	ASD	RVD	Re- ranking*	Precision	Recall	F1small	F1medium	F1large
LiTS–ISBI 2017	1	X. Han et al.	0.674 (1)	1.118 (3)	−0.103 (7)	11 (3)	0.354 (4)	0.458 (1)	0.103 (3)	0.450 (1)	0.879 (1)
	2	G. Chlebus et al.	0.652 (2)	1.076 (2)	−0.025 (2)	6 (2)	0.385 (3)	0.406 (4)	0.116 (1)	0.421 (3)	0.867 (3)
	3	E. Vorontsov et al.	0.645 (3)	1.225 (6)	−0.124 (8)	17 (6)	0.529 (2)	0.439 (2)	0.109 (2)	0.369 (4)	0.850 (4)
	4	L. Bi et al.	0.645 (3)	1.006 (1)	0.016 (1)	5 (1)	0.316 (5)	0.431 (3)	0.057 (5)	0.441 (2)	0.876 (2)
	5	C. Wang et al.	0.576 (4)	1.187 (5)	−0.073 (5)	14 (5)	0.273 (6)	0.346 (7)	0.038 (7)	0.323 (5)	0.806 (7)
	6	P. Christ et al.	0.529 (5)	1.130 (4)	0.031 (3)	12 (4)	0.552 (1)	0.383 (5)	0.075 (4)	0.221 (7)	0.837 (6)
	7	J. Lipkova et al.	0.476 (6)	2.366 (8)	−0.088 (6)	20 (7)	0.105 (8)	0.357 (6)	0.054 (6)	0.250 (6)	0.843 (5)
	8	J. Ma et al.	0.465 (7)	2.778 (10)	0.045 (4)	21 (8)	0.107 (7)	0.080 (10)	0.000 (10)	0.033 (10)	0.361 (10)
	9	T. Konopczynski et al.	0.417 (8)	1.342 (7)	−0.150 (9)	24 (9)	0.057 (9)	0.189 (8)	0.014 (9)	0.106 (8)	0.628 (8)
	10	M. Bellver et al.	0.411 (9)	2.776 (9)	−0.263 (11)	29 (10)	0.028 (10)	0.172 (9)	0.031 (8)	0.175 (9)	0.512 (9)
	11	J. Qi et al.	0.188 (10)	6.118 (11)	−0.229 (10)	31 (11)	0.008 (11)	0.009 (11)	0.000 (10)	0.000 (11)	0.041 (11)
LiTS–MICCAI 2017	1	J. Zou et al.	0.702 (1)	1.189 (8)	5.921 (10)	14 (2)	0.148 (13)	0.479 (2)	0.163 (2)	0.446 (4)	0.876 (5)
	2	X. Li et al.	0.686 (2)	1.073 (4)	5.164 (9)	16 (4)	0.426 (3)	0.515 (1)	0.150 (4)	0.544 (1)	0.907 (3)
	3	G. Chlebus et al.	0.676 (3)	1.143 (6)	0.464 (7)	16 (4)	0.519 (1)	0.463 (3)	0.129 (6)	0.494 (2)	0.836 (10)
	4	E. Vorontsov et al.	0.661 (4)	1.075 (5)	12.124 (15)	24 (6)	0.454 (2)	0.455 (6)	0.142 (5)	0.439 (6)	0.877 (4)
	5	Y. Yuan et al.	0.657 (5)	1.151 (7)	0.288 (6)	18 (5)	0.321 (5)	0.460 (5)	0.112 (8)	0.471 (3)	0.870 (8)
	6	J. Ma et al.	0.655 (6)	5.949 (12)	5.949 (11)	29 (9)	0.409 (4)	0.293 (14)	0.024 (13)	0.200 (13)	0.770 (13)
	7	K. Kaluva et al.	0.640 (7)	1.040 (2)	0.190 (4)	13 (1)	0.165 (10)	0.463 (4)	0.112 (7)	0.421 (8)	0.910 (1)
	8	X. Han et al.	0.630 (8)	1.050 (3)	0.170 (3)	14 (2)	0.160 (11)	0.330 (11)	0.129 (6)	0.411 (9)	0.908 (2)
	9	C. Wang et al.	0.625 (9)	1.260 (10)	8.300 (13)	32 (10)	0.156 (12)	0.408 (8)	0.081 (10)	0.423 (7)	0.832 (11)
	10	J. Wu et al.	0.624 (10)	1.232 (9)	4.679 (8)	27 (7)	0.179 (9)	0.372 (9)	0.093 (9)	0.373 (11)	0.875 (6)
	11	A. Ben-Cohen et al.	0.620 (11)	1.290 (11)	0.200 (5)	27 (7)	0.270 (7)	0.290 (15)	0.079 (11)	0.383 (10)	0.864 (9)
	12	L. Zhang et al.	0.620 (12)	1.388 (13)	6.420 (12)	37 (11)	0.239 (8)	0.446 (7)	0.152 (3)	0.445 (5)	0.872 (7)
	13	K. Roth et al.	0.570 (13)	0.950 (1)	0.020 (1)	15 (3)	0.070 (14)	0.300 (13)	0.167 (1)	0.411 (9)	0.786 (12)
	14	J. Lipkova et al.	0.480 (14)	1.330 (12)	0.060 (2)	28 (8)	0.060 (16)	0.190 (16)	0.014 (14)	0.206 (12)	0.755 (13)
	15	M. Piraud et al.	0.445 (15)	1.464 (14)	10.121 (14)	43 (12)	0.068 (15)	0.325 (12)	0.038 (12)	0.196 (14)	0.738 (15)
LiTS–MICCAI 2018	1	F. Isensee et al.	0.739 (1)	0.903 (2)	−0.074 (10)	13 (2)	0.502 (4)	0.554 (1)	0.239 (1)	0.564 (1)	0.915 (2)
	2	D. Xu et al.	0.721 (2)	0.896 (1)	−0.002 (1)	4 (1)	0.549 (2)	0.503 (2)	0.149 (3)	0.475 (2)	0.937 (1)
	3	S. Chen et al.	0.611 (3)	1.397 (11)	−0.113 (12)	26 (6)	0.182 (9)	0.368 (4)	0.035 (9)	0.239 (12)	0.859 (3)
	4	B. Park et al.	0.608 (4)	1.157 (6)	−0.067 (8)	18 (5)	0.343 (5)	0.350 (7)	0.044 (8)	0.267 (7)	0.845 (4)
	5	O. Kodym et al.	0.605 (5)	1.134 (4)	−0.048 (6)	16 (3)	0.523 (3)	0.336 (8)	0.063 (7)	0.243 (10)	0.819 (6)
	6	Z. Xu et al.	0.604 (6)	1.240 (8)	−0.025 (4)	18 (5)	0.396 (6)	0.334 (9)	0.015 (10)	0.243 (11)	0.837 (5)
	7	R. Chen et al.	0.569 (7)	1.238 (7)	−0.188 (14)	28 (8)	0.339 (7)	0.427 (3)	0.207 (2)	0.366 (3)	0.804 (8)
	8	I. Kim et al.	0.562 (8)	1.029 (3)	0.012 (2)	13 (2)	0.594 (1)	0.360 (5)	0.092 (4)	0.328 (5)	0.781 (9)
	9	M. Perslev et al.	0.556 (9)	1.134 (5)	0.020 (3)	17 (4)	0.024 (17)	0.330 (10)	0.034 (10)	0.251 (8)	0.811 (7)
	10	W. Bae et al.	0.517 (10)	1.650 (12)	−0.039 (5)	27 (7)	0.061 (14)	0.308 (11)	0.078 (6)	0.244 (9)	0.742 (11)
	11	I. Sarasua et al.	0.486 (11)	1.374 (10)	−0.084 (11)	32 (10)	0.043 (16)	0.298 (12)	0.045 (8)	0.294 (6)	0.678 (12)
	12	R. Rezaeifar et al.	0.472 (12)	1.776 (13)	−0.258 (15)	40 (12)	0.112 (11)	0.216 (13)	0.005 (12)	0.081 (14)	0.650 (13)
	13	O. Rippel et al.	0.451 (13)	1.345 (9)	−0.068 (9)	31 (9)	0.044 (15)	0.356 (6)	0.083 (5)	0.353 (4)	0.771 (10)
	14	S. Kim et al.	0.404 (14)	1.891 (14)	0.151 (13)	41 (13)	0.116 (10)	0.170 (14)	0.005 (12)	0.091 (13)	0.589 (14)
	15	F. Jia et al.	0.316 (15)	12.762 (16)	−0.620 (16)	47 (14)	0.069 (12)	0.011 (17)	0.015 (10)	0.000 (16)	0.024 (17)
	16	Y. Wang et al.	0.311 (16)	2.105 (15)	0.054 (7)	38 (11)	0.154 (8)	0.068 (15)	0.000 (13)	0.005 (15)	0.336 (15)
	17	J. Ma et al.	0.142 (17)	34.527 (17)	0.685 (17)	51 (15)	−0.066 (13)	0.013 (16)	0.000 (13)	0.000 (16)	0.049 (16)