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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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, 劉樹學. 手動圖像分割技術在嚴重膝關節骨性關節炎定量研究中的應用. 中國CT和MRI雜志, 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. 趙繼榮, 楊濤, 徐建成, 等. 自體骨髓抽吸濃縮物治療膝骨性關節炎的作用及機制. 中國組織工程研究, 2022, 26(18): 2938-2944.
2. 盧立軍. 人工膝關節置換術治療重度膝關節退行性骨關節病的效果分析. 中國實用醫藥, 2021, 16(31): 94-96.
3. 雷靜桃, 唐明瑤, 王君臣, 等. 機器人輔助膝關節置換術的術前規劃研究綜述. 機械工程學報, 2017, 53(17): 78-91.
4. 于寧波, 劉嘉男, 高麗, 等. 基于深度學習的膝關節MR圖像自動分割方法. 儀器儀表學報, 2020, 41(6): 140-149.
5. 宋平, 范哲奇, 智信, 等. 基于深度學習的膝關節CT圖像自動分割準確性驗證研究. 中國修復重建外科雜志, 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. ?ztürk 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. 馬巖, 邢藏菊, 肖亮. 基于級聯網絡的膝關節圖像分割與模型構建. 波譜學雜志, 2022, 39(2): 184-195.
16. 吳江平, 鄭馨. 一種針對膝關節CT圖像分割的卷積神經網絡. 現代電子技術, 2022, 45(18): 133-137.
17. 西北工業大學. 一種全膝關節有限元建模方法: 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. 陳清, 賈夢穎, 楊開雯, 等. 三維模型上膝關節中心的定位. 中國衛生標準管理, 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, 劉樹學. 手動圖像分割技術在嚴重膝關節骨性關節炎定量研究中的應用. 中國CT和MRI雜志, 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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