Study on the accuracy of automatic segmentation of knee CT images based on deep learning_Chinese Journal of Reparative and Reconstructive Surgery

Authors：

SONG Ping ^1,2,3 , FAN Zheqi ^1,2,3 , ZHI Xin ^2,3 , CAO Zheng ^2,3,4 , MIN Shengfeng ⁵ , LIU Xingyu ^5,6 , ZHANG Yiling ⁵ ,  KONG Xiangpeng ^2,3 ,  CHAI Wei ^2,3

1. Medical School of Chinese PLA General Hospital, Beijing, 100853, P. R. China;
2. Senior Department of Orthopedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing, 100048, P. R. China;
3. National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Beijing, 100853, P. R. China;
4. Medical School of Nankai University, Tianjin, 300071, P. R. China;
5. Longwood Valley Medical Technology Co. Ltd, Beijing, 100190, P. R. China;
6. College of Life Science, Tsinghua University, Beijing, 100084, P. R. China;

Corresponding author：

KONG Xiangpeng, Email: 18810999609@163.com; CHAI Wei, Email: chaiwei301@163.com

Keywords：

Artificial intelligence; deep learning; image segmentation; total knee arthroplasty

DOI：

10.7507/1002-1892.202201072

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy. Methods A knee CT scans database was established, and the bony structure was manually annotated. A deep learning neural network architecture was developed independently, and the labeled database was used to train and test the neural network. Metrics of Dice coefficient, average surface distance (ASD), and Hausdorff distance (HD) were calculated to evaluate the accuracy of the neural network. The time of automatic segmentation and manual segmentation was compared. Five orthopedic experts were invited to score the automatic and manual segmentation results using Likert scale and the scores of the two methods were compared. Results The automatic segmentation achieved a high accuracy. The Dice coefficient, ASD, and HD of the femur were 0.953±0.037, (0.076±0.048) mm, and (3.101±0.726) mm, respectively; and those of the tibia were 0.950±0.092, (0.083±0.101) mm, and (2.984±0.740) mm, respectively. The time of automatic segmentation was significantly shorter than that of manual segmentation [(2.46±0.45) minutes vs. (64.73±17.07) minutes; t=36.474, P<0.001). The clinical scores of the femur were 4.3±0.3 in the automatic segmentation group and 4.4±0.2 in the manual segmentation group, and the scores of the tibia were 4.5±0.2 and 4.5±0.3, respectively. There was no significant difference between the two groups (t=1.753, P=0.085; t=0.318, P=0.752). Conclusion The automatic segmentation of knee CT images based on deep learning has high accuracy and can achieve rapid segmentation and three-dimensional reconstruction. This method will promote the development of new technology-assisted techniques in total knee arthroplasty.

Citation： SONG Ping, FAN Zheqi, ZHI Xin, CAO Zheng, MIN Shengfeng, LIU Xingyu, ZHANG Yiling, KONG Xiangpeng, CHAI Wei. Study on the accuracy of automatic segmentation of knee CT images based on deep learning. Chinese Journal of Reparative and Reconstructive Surgery, 2022, 36(5): 534-539. doi: 10.7507/1002-1892.202201072 Copy

1.	Nakano N, Shoman H, Olavarria F, et al. Why are patients dissatisfied following a total knee replacement? A systematic review. Int Orthop, 2020, 44(10): 1971-2007.
2.	孙茂淋, 杨柳, 郭林, 等. 手术机器人辅助人工全膝关节置换术改善股骨旋转对线及早期疗效研究. 中国修复重建外科杂志, 2021, 35(7): 807-812.
3.	Batailler C, Fernandez A, Swan J, et al. MAKO CT-based robotic arm-assisted system is a reliable procedure for total knee arthroplasty: a systematic review. Knee Surg Sports Traumatol Arthrosc, 2021, 29(11): 3585-3598.
4.	Kayani B, Konan S, Tahmassebi J, et al. Robotic-arm assisted total knee arthroplasty is associated with improved early functional recovery and reduced time to hospital discharge compared with conventional jig-based total knee arthroplasty: a prospective cohort study. Bone Joint J, 2018, 100-B(7): 930-937.
5.	Kort N, Stirling P, Pilot P, et al. Robot-assisted knee arthroplasty improves component positioning and alignment, but results are inconclusive on whether it improves clinical scores or reduces complications and revisions: a systematic overview of meta-analyses. Knee Surg Sports Traumatol Arthrosc, 2021. doi: 10.1007/s00167-021-06472-4.
6.	Lei K, Liu L, Chen X, et al. Navigation and robotics improved alignment compared with PSI and conventional instrument, while clinical outcomes were similar in TKA: a network meta-analysis. Knee Surg Sports Traumatol Arthrosc, 2022, 30(2): 721-733.
7.	Gao J, Dong S, Li JJ, et al. New technology-based assistive techniques in total knee arthroplasty: A Bayesian network meta-analysis and systematic review. Int J Med Robot, 2020. doi: 10.1002/rcs.2189.
8.	Pietrzak JRT, Rowan FE, Kayani B, et al. Preoperative CT-based three-dimensional templating in robot-assisted total knee arthroplasty more accurately predicts implant sizes than two-dimensional templating. J Knee Surg, 2019, 32(7): 642-648.
9.	Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation//Lecture Notes in Computer Science. [s.l.]: Springer International Publishing, 2015: 234-241.
10.	Kirillov A, Wu Y, He K, et al. PointRend: Image segmentation as rendering. IEEE, 2020. doi: 10.1109/CVPR42600.2020.00982.
11.	Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging, 2015, 15: 29. doi: 10.1186/s12880-015-0068-x.
12.	Fenster A, Chiu B. Evaluation of segmentation algorithms for medical imaging. Conf Proc IEEE Eng Med Biol Soc, 2005, 2005: 7186-7189.
13.	Klein S, van der Heide UA, Lips IM, et al. Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med Phys, 2008, 35(4): 1407-1417.
14.	Neves CA, Tran ED, Kessler IM, et al. Fully automated preoperative segmentation of temporal bone structures from clinical CT scans. Sci Rep, 2021, 11(1): 116. doi: 10.1038/s41598-020-80619-0.
15.	Mahfouz MR, Abdel Fatah EE, Johnson JM, et al. A novel approach to 3D bone creation in minutes: 3D ultrasound. Bone Joint J, 2021, 103-B(6 Supple A): 81-86.
16.	Myers TG, Ramkumar PN, Ricciardi BF, et al. Artificial intelligence and orthopaedics: An introduction for clinicians. J Bone Joint Surg (Am), 2020, 102(9): 830-840.
17.	Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? J Arthroplasty, 2018, 33(8): 2358-2361.
18.	Haeberle HS, Helm JM, Navarro SM, et al. Artificial intelligence and machine learning in lower extremity arthroplasty: A review. J Arthroplasty, 2019, 34(10): 2201-2203.
19.	Li R, Xiao C, Huang Y, et al. Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: A review. Diagnostics (Basel), 2022, 12(2): 298. doi: 10.3390/diagnostics12020298.
20.	Sherer MV, Lin D, Elguindi S, et al. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother Oncol, 2021, 160: 185-191.
21.	Wu D, Sofka M, Birkbeck N, et al. Segmentation of multiple knee bones from CT for orthopedic knee surgery planning. Med Image Comput Comput Assist Interv, 2014, 17(Pt 1): 372-380.

1. Nakano N, Shoman H, Olavarria F, et al. Why are patients dissatisfied following a total knee replacement? A systematic review. Int Orthop, 2020, 44(10): 1971-2007.
2. 孙茂淋, 杨柳, 郭林, 等. 手术机器人辅助人工全膝关节置换术改善股骨旋转对线及早期疗效研究. 中国修复重建外科杂志, 2021, 35(7): 807-812.
3. Batailler C, Fernandez A, Swan J, et al. MAKO CT-based robotic arm-assisted system is a reliable procedure for total knee arthroplasty: a systematic review. Knee Surg Sports Traumatol Arthrosc, 2021, 29(11): 3585-3598.
4. Kayani B, Konan S, Tahmassebi J, et al. Robotic-arm assisted total knee arthroplasty is associated with improved early functional recovery and reduced time to hospital discharge compared with conventional jig-based total knee arthroplasty: a prospective cohort study. Bone Joint J, 2018, 100-B(7): 930-937.
5. Kort N, Stirling P, Pilot P, et al. Robot-assisted knee arthroplasty improves component positioning and alignment, but results are inconclusive on whether it improves clinical scores or reduces complications and revisions: a systematic overview of meta-analyses. Knee Surg Sports Traumatol Arthrosc, 2021. doi: 10.1007/s00167-021-06472-4.
6. Lei K, Liu L, Chen X, et al. Navigation and robotics improved alignment compared with PSI and conventional instrument, while clinical outcomes were similar in TKA: a network meta-analysis. Knee Surg Sports Traumatol Arthrosc, 2022, 30(2): 721-733.
7. Gao J, Dong S, Li JJ, et al. New technology-based assistive techniques in total knee arthroplasty: A Bayesian network meta-analysis and systematic review. Int J Med Robot, 2020. doi: 10.1002/rcs.2189.
8. Pietrzak JRT, Rowan FE, Kayani B, et al. Preoperative CT-based three-dimensional templating in robot-assisted total knee arthroplasty more accurately predicts implant sizes than two-dimensional templating. J Knee Surg, 2019, 32(7): 642-648.
9. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation//Lecture Notes in Computer Science. [s.l.]: Springer International Publishing, 2015: 234-241.
10. Kirillov A, Wu Y, He K, et al. PointRend: Image segmentation as rendering. IEEE, 2020. doi: 10.1109/CVPR42600.2020.00982.
11. Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging, 2015, 15: 29. doi: 10.1186/s12880-015-0068-x.
12. Fenster A, Chiu B. Evaluation of segmentation algorithms for medical imaging. Conf Proc IEEE Eng Med Biol Soc, 2005, 2005: 7186-7189.
13. Klein S, van der Heide UA, Lips IM, et al. Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med Phys, 2008, 35(4): 1407-1417.
14. Neves CA, Tran ED, Kessler IM, et al. Fully automated preoperative segmentation of temporal bone structures from clinical CT scans. Sci Rep, 2021, 11(1): 116. doi: 10.1038/s41598-020-80619-0.
15. Mahfouz MR, Abdel Fatah EE, Johnson JM, et al. A novel approach to 3D bone creation in minutes: 3D ultrasound. Bone Joint J, 2021, 103-B(6 Supple A): 81-86.
16. Myers TG, Ramkumar PN, Ricciardi BF, et al. Artificial intelligence and orthopaedics: An introduction for clinicians. J Bone Joint Surg (Am), 2020, 102(9): 830-840.
17. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? J Arthroplasty, 2018, 33(8): 2358-2361.
18. Haeberle HS, Helm JM, Navarro SM, et al. Artificial intelligence and machine learning in lower extremity arthroplasty: A review. J Arthroplasty, 2019, 34(10): 2201-2203.
19. Li R, Xiao C, Huang Y, et al. Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: A review. Diagnostics (Basel), 2022, 12(2): 298. doi: 10.3390/diagnostics12020298.
20. Sherer MV, Lin D, Elguindi S, et al. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother Oncol, 2021, 160: 185-191.
21. Wu D, Sofka M, Birkbeck N, et al. Segmentation of multiple knee bones from CT for orthopedic knee surgery planning. Med Image Comput Comput Assist Interv, 2014, 17(Pt 1): 372-380.

Chinese Journal of Reparative and Reconstructive Surgery

Study on the accuracy of automatic segmentation of knee CT images based on deep learning

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content