The development and primary application of a deep learning convolutional neural network in the field of revision total hip arthroplasty CT segmentation
10.3760/cma.j.cn121113-20220923-00578
- VernacularTitle:深度学习卷积神经网络在髋关节翻修术前CT分割领域的研发与初步应用
- Author:
Dong WU
1
;
Xiangpeng KONG
;
Minzhi YANG
;
Xingyu LIU
;
Yiling ZHANG
;
Wei CHAI
Author Information
1. 解放军总医院第四医学中心，北京 100853
- Keywords:
Arthroplasty, replacement, hip;
Reoperation;
Artificial intelligence;
Neural networks, computer;
Multidetector computed tomography
- From:
Chinese Journal of Orthopaedics
2023;43(1):62-71
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a preoperative CT image segmentation algorithm based on artificial intelligence deep learning technology for total hip arthroplasty (THA) revision surgery, and to verify and preliminarily apply it.Methods:A total of 706 revision cases with clear CT data from April 2019 to October 2022 in Chinese PLA General Hospital were retrospectively analyzed, including 520 males, aged 58.45±18.13 years, and 186 females, aged 52.23±16.23 years. All of them were unilateral, and there were 402 hips on the left and 304 hips on the right. The transformer_unet convolutional neural network was constructed and trained using Tensorflow 1.15 to achieve intelligent segmentation of the revision THA CT images. Based on the developed three-dimensional planning system of total hip arthroplasty, an intelligent planning system for revision hip arthroplasty was preliminarily constructed. Dice overlap coefficient (DOC), average surface distance (ASD) and Hausdorff distance (HD) parameters were used to evaluate the segmentation accuracy of transformer_unet, full convolution network (FCN), 2D U-shaped Net and Deeplab v3 +, and segmentation time was used to evaluate the segmentation efficiency of these networks.Results:Compared with the FCN, 2D U-Net, and Deeplab v3+ learning curves, the transformer_unet network could achieve better training effect with less training amount.The DOC of transformer_unet was 95%±4%, the HD was 3.35±1.03 mm, and the ASD was 1.38±0.02 mm; FCN was 94%±4%, 4.83±1.90 mm, 1.42±0.03 mm; 2D U-Net was 93%±5%, 5.27±2.20 mm, and 1.46±0.02 mm, respectively. Deeplab v3+ was 92%±4%, 6.12±1.84 mm, 1.52±0.03 mm, respectively. The transformer_unet coefficients were better than those of the other three convolutional neural networks, and the differences were statistically significant (all P<0.05). The segmentation time of transformer_unet was 0.031±0.001 s, FCN was 0.038±0.002 s, 2D U-Net was 0.042±0.001 s, Deeplab v3+ was 0.048±0.002 s. The segmentation time of transformer_unet was less than that of the other three convolutional neural networks, and the difference was statistically significant ( P<0.05). Based on the results of previous studies, an artificial intelligence assisted preoperative planning system for THA revision surgery was initially constructed. Conclusion:Compared with FCN, 2D U-Net and Deeplab v3+, the transformer_unet convolutional neural network can complete the segmentation of the revision THA CT image more accurately and efficiently, which is expected to provide technical support for preoperative planning and surgical robots.
