Automatic modeling of the knee joint based on artificial intelligence_Chinese Journal of Reparative and Reconstructive Surgery

Authors：

TANG Xiaoyong , LI Xiaohu ,  GU Xuelian , ZHAO Yuxuan , LIU Anchen , LIU Yutian , TAO Yurong

School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, P. R. China;

Corresponding author：

GU Xuelian, Email: guxuelian@usst.edu.cn

Keywords：

Automatic segmentation; knee joint; Pearson coefficient; DICE coefficient; artificial intelligence

DOI：

10.7507/1002-1892.202212008

Video：

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Objective To investigate an artificial intelligence (AI) automatic segmentation and modeling method for knee joints, aiming to improve the efficiency of knee joint modeling. Methods Knee CT images of 3 volunteers were randomly selected. AI automatic segmentation and manual segmentation of images and modeling were performed in Mimics software. The AI-automated modeling time was recorded. The anatomical landmarks of the distal femur and proximal tibia were selected with reference to previous literature, and the indexes related to the surgical design were calculated. Pearson correlation coefficient (r) was used to judge the correlation of the modeling results of the two methods; the consistency of the modeling results of the two methods were analyzed by DICE coefficient. Results The three-dimensional model of the knee joint was successfully constructed by both automatic modeling and manual modeling. The time required for AI to reconstruct each knee model was 10.45, 9.50, and 10.20 minutes, respectively, which was shorter than the manual modeling [(64.73±17.07) minutes] in the previous literature. Pearson correlation analysis showed that there was a strong correlation between the models generated by manual and automatic segmentation (r=0.999, P<0.001). The DICE coefficients of the 3 knee models were 0.990, 0.996, and 0.944 for the femur and 0.943, 0.978, and 0.981 for the tibia, respectively, verifying a high degree of consistency between automatic modeling and manual modeling. Conclusion The AI segmentation method in Mimics software can be used to quickly reconstruct a valid knee model.

Citation： TANG Xiaoyong, LI Xiaohu, GU Xuelian, ZHAO Yuxuan, LIU Anchen, LIU Yutian, TAO Yurong. Automatic modeling of the knee joint based on artificial intelligence. Chinese Journal of Reparative and Reconstructive Surgery, 2023, 37(3): 348-352. doi: 10.7507/1002-1892.202212008 Copy

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14.	Zhou Z, Zhao G, Kijowski R, et al. Deep convolutional neural network for segmentation of knee joint anatomy. Magn Reson Med, 2018, 80(6): 2759-2770.
15.	马岩, 邢藏菊, 肖亮. 基于级联网络的膝关节图像分割与模型构建. 波谱学杂志, 2022, 39(2): 184-195.
16.	吴江平, 郑馨. 一种针对膝关节CT图像分割的卷积神经网络. 现代电子技术, 2022, 45(18): 133-137.
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18.	Victor J, Van Doninck D, Labey L, et al. How precise can bony landmarks be determined on a CT scan of the knee? Knee, 2009, 16(5): 358-365.
19.	陈清, 贾梦颖, 杨开雯, 等. 三维模型上膝关节中心的定位. 中国卫生标准管理, 2020, 11(19): 82-84.
20.	Bori E, Pancani S, Vigliotta S, et al. Validation and accuracy evaluation of automatic segmentation for knee joint pre-planning. Knee, 2021, 33: 275-281.
21.	Qiu B, Guo J, Kraeima J, et al. Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network. Phys Med Biol, 2019, 64(17): 175020. doi: 10.1088/1361-6560/ab2c95.
22.	曹明明, 刘树学. 手动图像分割技术在严重膝关节骨性关节炎定量研究中的应用. 中国CT和MRI杂志, 2019, 17(5): 133-136.
23.	Lo Presti G, Carbone M, Ciriaci D, et al. Assessment of DICOM viewers capable of loading patient-specific 3D models obtained by different segmentation platforms in the operating room. J Digit Imaging, 2015, 28(5): 518-527.
24.	An G, Hong L, Zhou XB, et al. Accuracy and efficiency of computer-aided anatomical analysis using 3D visualization software based on semi-automated and automated segmentations. Ann Anat, 2017, 210: 76-83.

1. 赵继荣, 杨涛, 徐建成, 等. 自体骨髓抽吸浓缩物治疗膝骨性关节炎的作用及机制. 中国组织工程研究, 2022, 26(18): 2938-2944.
2. 卢立军. 人工膝关节置换术治疗重度膝关节退行性骨关节病的效果分析. 中国实用医药, 2021, 16(31): 94-96.
3. 雷静桃, 唐明瑶, 王君臣, 等. 机器人辅助膝关节置换术的术前规划研究综述. 机械工程学报, 2017, 53(17): 78-91.
4. 于宁波, 刘嘉男, 高丽, 等. 基于深度学习的膝关节MR图像自动分割方法. 仪器仪表学报, 2020, 41(6): 140-149.
5. 宋平, 范哲奇, 智信, 等. 基于深度学习的膝关节CT图像自动分割准确性验证研究. 中国修复重建外科杂志, 2022, 36(5): 534-539.
6. Friedli L, Kloukos D, Kanavakis G, et al. The effect of threshold level on bone segmentation of cranial base structures from CT and CBCT images. Sci Rep, 2020, 10(1): 7361. doi: 10.1038/s41598-020-64383-9.
7. Öztürk CN, Albayrak S. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling. Comput Biol Med, 2016, 72: 90-107.
8. Tang J, Millington S, Acton ST, et al. Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes. IEEE Trans Biomed Eng, 2006, 53(5): 896-907.
9. Williams TG, Holmes AP, Waterton JC, et al. Anatomically corresponded regional analysis of cartilage in asymptomatic and osteoarthritic knees by statistical shape modelling of the bone. IEEE Trans Med Imaging, 2010, 29(8): 1541-1559.
10. Shan L, Zach C, Charles C, et al. Automatic atlas-based three-label cartilage segmentation from MR knee images. Med Image Anal, 2014, 18(7): 1233-1246.
11. Zhang K, Lu W, Marziliano P. Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies. Magn Reson Imaging, 2013, 31(10): 1731-1743.
12. Norman B, Pedoia V, Majumdar S. Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry. Radiology, 2018, 288(1): 177-185.
13. Ronneberger O. Invited Talk: U-Net convolutional networks for biomedical image segmentation//Bildverarbeitung für die Medizin. Berlin: Springer Vieweg, 2017. < a href=" ">https://doi.org/10.1007/978-3-662-54345-0_3.
14. Zhou Z, Zhao G, Kijowski R, et al. Deep convolutional neural network for segmentation of knee joint anatomy. Magn Reson Med, 2018, 80(6): 2759-2770.
15. 马岩, 邢藏菊, 肖亮. 基于级联网络的膝关节图像分割与模型构建. 波谱学杂志, 2022, 39(2): 184-195.
16. 吴江平, 郑馨. 一种针对膝关节CT图像分割的卷积神经网络. 现代电子技术, 2022, 45(18): 133-137.
17. 西北工业大学. 一种全膝关节有限元建模方法: CN201910780245.3[P]. 2019-11-12.
18. Victor J, Van Doninck D, Labey L, et al. How precise can bony landmarks be determined on a CT scan of the knee? Knee, 2009, 16(5): 358-365.
19. 陈清, 贾梦颖, 杨开雯, 等. 三维模型上膝关节中心的定位. 中国卫生标准管理, 2020, 11(19): 82-84.
20. Bori E, Pancani S, Vigliotta S, et al. Validation and accuracy evaluation of automatic segmentation for knee joint pre-planning. Knee, 2021, 33: 275-281.
21. Qiu B, Guo J, Kraeima J, et al. Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network. Phys Med Biol, 2019, 64(17): 175020. doi: 10.1088/1361-6560/ab2c95.
22. 曹明明, 刘树学. 手动图像分割技术在严重膝关节骨性关节炎定量研究中的应用. 中国CT和MRI杂志, 2019, 17(5): 133-136.
23. Lo Presti G, Carbone M, Ciriaci D, et al. Assessment of DICOM viewers capable of loading patient-specific 3D models obtained by different segmentation platforms in the operating room. J Digit Imaging, 2015, 28(5): 518-527.
24. An G, Hong L, Zhou XB, et al. Accuracy and efficiency of computer-aided anatomical analysis using 3D visualization software based on semi-automated and automated segmentations. Ann Anat, 2017, 210: 76-83.

Chinese Journal of Reparative and Reconstructive Surgery

Automatic modeling of the knee joint based on artificial intelligence

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