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Proximal femur parameter measurement via improved PointNet++

分割 计算机科学 人工智能 Sørensen–骰子系数 股骨 重复性 股骨头 精确性和召回率 特征(语言学) 噪音(视频) 计算机视觉 模式识别(心理学) 口腔正畸科 图像分割 医学 外科 数学 统计 图像(数学) 哲学 语言学
作者
Jiayu Yang,Zhe Li,Pengyu Zhan,Xinghua Li,Kunzheng Wang,Jiawei Han,Pei Yang
出处
期刊:International Journal of Medical Robotics and Computer Assisted Surgery [Wiley]
卷期号:19 (3) 被引量:1
标识
DOI:10.1002/rcs.2494
摘要

Abstract Background Femoral morphological studies and parameter measurements play a crucial role in diagnosing hip joint disease, preoperative planning for total hip arthroplasty, and prosthesis design. Doctors usually perform parameter measurements manually in clinical practice, but it is time‐consuming and labor‐intensive. Moreover, the results rely heavily on the doctor's experience, and the repeatability is poor. Therefore, the accurate and automatic measurement methods of proximal femoral parameters are of great value. Method We collected 300 cases of clinical CT data of the femur. We introduced the adaptive function adjustment module to the neural network PointNet++ to strengthen the global feature extraction of the point cloud for improving the accuracy of femur segmentation. We used the improved PointNet++ network to segment the femur into three parts: femoral head, femoral neck, and femoral shaft. We evaluated the segmentation accracy using Dice Coefficient, MIoU, recall, and precision indicators. We achieved the automatic measurement of the proximal femoral parameters using the shape fitting algorithms, and compared the automatic and manual measurement results. Results The Dice, MIoU, recall and precision indicator of the improved segmentation algorithm reached 98.05%, 96.55%, 96.63%, and 96.03%, respectively. The comparison between automatic and manual measurement results showed that the mean accuracies of all parameters were above 95%, the mean errors were less than 5 mm and 3°, and the ICC values were more than 0.8, indicating that the automatic measurement results were accurate. Conclusion Our improved PointNet++ network provided high‐precision segmentation of the femur. We further completed automatic measurement of the femur parameters and verified its high accuracy. This method is of great value for the diagnosis and preoperative planning of hip diseases.
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