基于DBCNet的TOF-MRA中脑动脉树区域自动分割方法

张嘉骏,鲁宇澄,鲍奕仿,李郁欣,耿辰,胡伏原,戴亚康

An Automatic Segmentation Method of Cerebral Arterial Tree in TOF-MRA Based on DBCNet

ZHANG Jiajun,LU Yucheng,BAO Yifang,LI Yuxin,GENG Chen,HU Fuyuan,DAI Yakang

表3 利用DBCNet与常见深度学习网络对测试集数据进行分割的性能评估

Table 3 Segmentation performance evaluation of each region of the intracranial arterial tree in the testing data set using DBCNet and other common deep learning networks

		ACA	BA	ICA	MCA	PCA	VA	平均
nnUNet	Dice/%	52.18±2.29	0	80.03±5.93	57.00±2.15	45.00±15.81	0	26.78±3.91
nnUNet	HD95/mm	31.74±8.50	30	8.29±11.01	21.91±14.10	27.15±4.08	30	24.84±3.94
Modified UNet	Dice/%	77.89±5.14	81.01±7.22	89.49±2.67	76.26±2.94	0	0	49.90±3.59
Modified UNet	HD95/mm	6.48±4.48	8.49±14.01	4.95±10.67	7.99±3.32	30	30	14.65±2.69
VNet	Dice/%	58.70±22.74	73.56±7.79	89.46±3.81	70.86±4.16	72.97±5.66	70.04±14.56	54.56±8.59
VNet	HD95/mm	13.56±8.43	12.59±14.59	5.16±11.03	15.76±4.84	22.81±6.57	14.33±6.69	14.04±8.69
DBCNet	Dice/%	86.32±4.59	81.56±3.54	92.52±1.25	86.53±3.44	81.66±3.15	82.92±6.28	74.72±3.36
DBCNet	HD95/mm	3.30±2.32	4.94±2.97	1.27±0.87	3.64±2.41	5.16±3.02	5.05±5.85	3.89±1.30

注：其中加粗表示最优结果.当区域在个别或全部影像中无分割结果时，按照Dice=0且HD95=30 mm计算，30为测试集评估结果中的最大值向上取整得到.