Research on CT Image Segmentation Algorithm of Abdominal Artery Based on U-Shaped
Fully Convolutional Neural Network
ZHAO Xiulan1,LIU Yinwen2
1. CT Room, Haoji Center Health Center, Leping Town, Chiping County, Liaocheng Shandong 252126, China|
2. CT Room, Hospital of Traditional Chinese Medicine of Dongchangfu District, Liaocheng Shandong 252003, China
Abstract:Objective To propose an automatic CT image segmentation algorithm of abdominal artery based on deep learning, in order
to segment abdominal artery accurately. Methods The CT image features of cross section, coronal plane and sagittal plane of vessels
were extracted by using the method of area imbalance patch generation. Then, the U-shaped fully convolutional neural network
(U-Net) was adopted to train and segment the patch features. Finally, the three-dimension segmentation image was achieved by
using the maximum voxel preservation method. 120 cases of abdominal CT vascular images were selected for network training and
segmentation experiments. The segmentation accuracy was measured by precision rate, recall rate, and Dice coefficient. Results All large
vessels and most small vessels in abdominal CT images could be segmented based on U-Net method. The average Dice coefficient,
precision rate, and recall rate of U-Net with patch size s=32 were 87.2%, 85.9% and 88.5% respectively, which were approximately
equal to patch size s=48 and s=64. Moreover, the average Dice coefficient, precision rate, and recall rate of the proposed method
based on U-Net were better than other vessel segmentation algorithms. Conclusion The segmentation accuracy of image based on
U-Net method is high, which is a feasible abdominal CT vascular segmentation algorithm.
赵秀兰1,刘印文2. 基于U型全卷积神经网络的腹部动脉CT图像分割算法研究[J]. 中国医疗设备, 2021, 36(2): 85-88.
ZHAO Xiulan1,LIU Yinwen2. Research on CT Image Segmentation Algorithm of Abdominal Artery Based on U-Shaped
Fully Convolutional Neural Network. China Medical Devices, 2021, 36(2): 85-88.