Ping Song1,2,3, Zheqi Fan1,2,3, Xin Zhi2,3, Zheng Cao2,3,4, Shengfeng Min5, Xingyu Liu5,6, Yiling Zhang5, Xiangpeng Kong2,3, Wei Chai2,3. 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.
Abstract
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.
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.
Entities:
Keywords:
Artificial intelligence; deep learning; image segmentation; total knee arthroplasty
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