Reproducing the Hip Anatomy: Intraoperative Planning and Assistive Devices (CAS, Robotics)

Dettmer, Marius; Kreuzer, Stefan; Malanka, Stefany J. K.

doi:10.1007/978-3-030-24243-5_10

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…2 Appropriate position and placement of femoral and acetabular component is an important factor affecting the postoperative outcomes of THA. 3 However, the mismatch between the prosthesis and the hip anatomy might be one of the reasons for the poor placement of the prosthesis, which often resulted in the prosthesis loosening and the surgery failure. 4,5 As far as individuals are concerned, the femur morphology varies greatly and is easily affected by many factors, such as genetics, living environment, work and rest habits, and gender, resulting in differences in proximal femoral parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Although THA has achieved a 10‐year survival rate of 95% and a 25‐year survival rate of 80% successfully, there are still issues such as patient dissatisfaction and early revision 2 . Appropriate position and placement of femoral and acetabular component is an important factor affecting the postoperative outcomes of THA 3 . However, the mismatch between the prosthesis and the hip anatomy might be one of the reasons for the poor placement of the prosthesis, which often resulted in the prosthesis loosening and the surgery failure 4,5 .…”

Section: Introductionmentioning

confidence: 99%

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Proximal femur parameter measurement via improved PointNet++

Yang

Zhan

et al. 2023

Robotics Computer Surgery

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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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Section: Introductionmentioning

confidence: 99%