An automatic measurement system of distal femur morphological parameters using 3D slicer software

Chen, Zhen; Wang, Yagang; Li, Xinghua; Wang, Kunzheng; Li, Zhe; Yang, Pei

doi:10.1016/j.bone.2021.116300

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…Notably, Liao et al [22] applied these methods for cranial surgery planning, while Cao et al employed 3D Slicer for the volumetric analysis of intracranial hemorrhages [38]. Shi et al also utilized 3D Slicer for preoperative assessments [43,45,47,50]. Additionally, Ari et al [10] and Brown et al [11] leveraged 3D Slicer for extracting and analyzing omics features.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Zhang,

Feng,

Zhao

et al. 2024

Diagnostics

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Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, has become a valuable tool in medical imaging education due to its integration of various AI applications. Through its open-source architecture, students can gain practical experience with diverse medical images and the latest AI technology, reinforcing their understanding of anatomy and imaging technology while fostering independent learning and clinical reasoning skills. The implementation of this platform improves instruction quality and nurtures skilled professionals who can meet the demands of clinical practice, research institutions, and technology innovation enterprises. AI algorithms’ application in medical image processing have facilitated their translation from the lab to practical clinical applications and education.

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Section: Discussionmentioning

confidence: 99%

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Zhang,

Feng,

Zhao

et al. 2024

Diagnostics

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“…However, the preliminary preparation work such as using software to derive the model and unify coordinate system requires lots of manual assistance. Chen et al 16 used the registration algorithm to unify all femur models into the same coordinate system, and then used the 3D Slicer software to match the points artificially marked by doctors to realize the automatic measurement system of the distal femur. However, the marker points were manually marked by doctors, which was somewhat subjective.…”

Section: Dicementioning

confidence: 99%

“…Hu et al 17 Many studies have reported automatic measurement methods based on points in the external profile, such as knee parameters. 16 In this study, we achieved a measurement of hip parameters based on the centroids and mathematical features of the bone structure rather than the external profile point, which comprehensively complements the bone measurement. One limitation of this study was that there were still some large segmentation and measurement errors for some outlier models of deformed femurs.…”

Section: Dicementioning

confidence: 99%

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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Statistical modeling: Assessing the anatomic variability of knee joint space width

Jiang

et al. 2023

Journal of Biomechanics

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An automatic measurement system of distal femur morphological parameters using 3D slicer software

Cited by 7 publications

References 44 publications

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Proximal femur parameter measurement via improved PointNet++

Statistical modeling: Assessing the anatomic variability of knee joint space width

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