• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Automatic Femoral Deformity Analysis Based on the Constrained Local Models and Hough Forest.基于约束局部模型和 Hough 森林的自动股骨畸形分析
J Digit Imaging. 2022 Apr;35(2):162-172. doi: 10.1007/s10278-021-00550-2. Epub 2022 Jan 10.
2
Radiology of adolescent slipped capital femoral epiphysis: measurement of epiphyseal angles and diagnosis.青少年股骨头骨骺滑脱的放射学:骨骺角的测量与诊断
Oper Orthop Traumatol. 2007 Oct;19(4):329-44. doi: 10.1007/s00064-007-1214-6.
3
Proximal femur parameter measurement via improved PointNet+.经改良的 PointNet+进行股骨近端参数测量。
Int J Med Robot. 2023 Jun;19(3):e2494. doi: 10.1002/rcs.2494. Epub 2023 Jan 4.
4
Differences in Femoral Torsion Among Various Measurement Methods Increase in Hips With Excessive Femoral Torsion.各种测量方法在股骨扭转中的差异在股骨扭转过度的髋关节中增加。
Clin Orthop Relat Res. 2019 May;477(5):1073-1083. doi: 10.1097/CORR.0000000000000610.
5
Geometry of the Valgus Knee: Contradicting the Dogma of a Femoral-Based Deformity.外翻膝的几何学:与基于股骨畸形的教条相矛盾。
Am J Sports Med. 2017 Mar;45(4):909-914. doi: 10.1177/0363546516676266. Epub 2016 Dec 21.
6
Femoral deformity planning: intentional placement of the apex of deformity.股骨畸形规划:畸形顶点的有意放置。
Orthopedics. 2013 May;36(5):e533-7. doi: 10.3928/01477447-20130426-11.
7
A new scheme for automatic 2D detection of spheric and aspheric femoral heads: A case study on coronal MR images of bilateral hip joints of patients with Legg-Calve-Perthes disease.一种用于自动检测球形和非球形股骨头的新方案:基于 Legg-Calve-Perthes 病患者双侧髋关节冠状位 MRI 的病例研究。
Comput Methods Programs Biomed. 2019 Jul;175:83-93. doi: 10.1016/j.cmpb.2019.04.001. Epub 2019 Apr 1.
8
FACTS: Fully Automatic CT Segmentation of a Hip Joint.事实:髋关节的全自动CT分割
Ann Biomed Eng. 2015 May;43(5):1247-59. doi: 10.1007/s10439-014-1176-4. Epub 2014 Nov 4.
9
Comparison of Three Circular Frames in Lower Limb Deformity Correction: A Biomechanical Study.三种环形框架在下肢体畸形矫正中的比较:一项生物力学研究。
Cureus. 2022 May 24;14(5):e25271. doi: 10.7759/cureus.25271. eCollection 2022 May.
10
A novel approach for computerized quantitative image analysis of proximal femur bone shape deformities based on the hip joint symmetry.基于髋关节对称性的计算机定量图像分析股骨近端骨形状畸形的新方法。
Artif Intell Med. 2021 May;115:102057. doi: 10.1016/j.artmed.2021.102057. Epub 2021 Mar 24.

本文引用的文献

1
A Semi-automatic Diagnosis of Hip Dysplasia on X-Ray Films.X线片上髋关节发育不良的半自动诊断
Front Mol Biosci. 2020 Dec 17;7:613878. doi: 10.3389/fmolb.2020.613878. eCollection 2020.
2
Optimization of electronic prescription for parallel external fixator based on genetic algorithm.基于遗传算法的电子处方平行外固定器优化。
Int J Comput Assist Radiol Surg. 2019 May;14(5):861-871. doi: 10.1007/s11548-019-01931-3. Epub 2019 Mar 18.
3
Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.基于深度卷积神经网络的股骨近端磁共振图像分割。
Sci Rep. 2018 Nov 7;8(1):16485. doi: 10.1038/s41598-018-34817-6.
4
Femur segmentation in DXA imaging using a machine learning decision tree.基于机器学习决策树的 DXA 影像中股骨分割
J Xray Sci Technol. 2018;26(5):727-746. doi: 10.3233/XST-180399.
5
Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting.基于随机森林回归投票的稳健精确形状模型匹配。
IEEE Trans Pattern Anal Mach Intell. 2015 Sep;37(9):1862-74. doi: 10.1109/TPAMI.2014.2382106.
6
Mechanical, Anatomical, and Kinematic Axis in TKA: Concepts and Practical Applications.全膝关节置换术中的机械轴、解剖轴和运动轴:概念与实际应用。
Curr Rev Musculoskelet Med. 2014 Jun;7(2):89-95. doi: 10.1007/s12178-014-9218-y.
7
Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.基于统计模型的数字前后位(AP)骨盆 X 线片中股骨近端的分割。
Int J Comput Assist Radiol Surg. 2014 Mar;9(2):165-76. doi: 10.1007/s11548-013-0932-5. Epub 2013 Jul 31.
8
Fully automatic segmentation of the proximal femur using random forest regression voting.基于随机森林回归投票的股骨近端全自动分割。
IEEE Trans Med Imaging. 2013 Aug;32(8):1462-72. doi: 10.1109/TMI.2013.2258030. Epub 2013 Apr 12.
9
Clinical value of the Taylor Spatial Frame: a comparison with the Ilizarov and Orthofix fixators.泰勒空间外固定架的临床价值:与伊利扎罗夫外固定架和奥托芬克斯外固定架的比较
J Child Orthop. 2011 Oct;5(5):343-9. doi: 10.1007/s11832-011-0361-3. Epub 2011 Aug 19.
10
Validation of a statistical shape model-based 2D/3D reconstruction method for determination of cup orientation after THA.基于统计形状模型的 2D/3D 重建方法验证用于确定 THA 后的杯方位。
Int J Comput Assist Radiol Surg. 2012 Mar;7(2):225-31. doi: 10.1007/s11548-011-0644-7. Epub 2011 Jul 27.

基于约束局部模型和 Hough 森林的自动股骨畸形分析

Automatic Femoral Deformity Analysis Based on the Constrained Local Models and Hough Forest.

机构信息

School of Artificial Intelligence and Data Science, Hebei University of Technology, No. 8 Guangrong Road, Hong Qiao, Tianjin, 300130, China.

Rehabilitation Hospital, National Research Center for Rehabilitation Technical Aids, No. 1 Ronghua Middle Road, Da Xing, Beijing, 100176, China.

出版信息

J Digit Imaging. 2022 Apr;35(2):162-172. doi: 10.1007/s10278-021-00550-2. Epub 2022 Jan 10.

DOI:10.1007/s10278-021-00550-2
PMID:35013828
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8921433/
Abstract

Clinically, Taylor spatial frame (TSF) is usually used to correct femoral deformity. The first step in correction is to analyze skeletal deformities and measure the center of rotation of angulation (CORA). Since the above work needs to be done manually, the doctor's workload is heavy. Therefore, an automatic femoral deformity analysis system was proposed. Firstly, the Hough forest and constrained local models were trained on the femur image set. Then, the position and size of the femur in the X-ray image were detected by the trained Hough forest. Furthermore, the position and size were served as the initial values of the trained constrained local models to fit the femoral contour. Finally, the anatomical axis line of the proximal femur and the anatomical axis line of the distal femur could be drawn according to the fitting results. According to these lines, CORA can be found. Compared with manual measurement by doctors, the average error of the hip joint orientation line was 1.7°, the standard deviation was 1.75, the average error of the anatomic axis line of the proximal femur was 2.9°, and the standard deviation was 3.57. The automatic femoral deformity analysis system meets the accuracy requirements of orthopedics and can significantly reduce the workload of doctors.

摘要

临床上,Taylor 空间框架(TSF)通常用于矫正股骨畸形。矫正的第一步是分析骨骼畸形并测量成角旋转中心(CORA)。由于上述工作需要手动完成,医生的工作量很大。因此,提出了一种自动股骨畸形分析系统。首先,在股骨图像集上训练 Hough 森林和约束局部模型。然后,通过训练的 Hough 森林检测 X 射线图像中股骨的位置和大小。此外,位置和大小被用作训练的约束局部模型的初始值,以拟合股骨轮廓。最后,根据拟合结果绘制近端股骨解剖轴线和远端股骨解剖轴线。根据这些线,可以找到 CORA。与医生的手动测量相比,髋关节定向线的平均误差为 1.7°,标准差为 1.75,近端股骨解剖轴线的平均误差为 2.9°,标准差为 3.57。自动股骨畸形分析系统满足矫形学的精度要求,可以显著减轻医生的工作量。