Literature DB >> 35013828

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

Lunhui Duan1, Hao Sun2, Delong Liu1, Yinglun Tan1, Yue Guo3,4, Jianwen Chen3,4, Xiaojing Ding5.   

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.
© 2021. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.

Entities:  

Keywords:  Computer-aided detection/diagnosis; Constrained local models; Femoral deformity analysis; Hough forest; Image segmentation; X-ray

Mesh:

Year:  2022        PMID: 35013828      PMCID: PMC8921433          DOI: 10.1007/s10278-021-00550-2

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  13 in total

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Authors:  Claudia Lindner; Paul A Bromiley; Mircea C Ionita; Tim F Cootes
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2015-09       Impact factor: 6.226

Review 2.  Perioperative planning for two- and three-plane deformities.

Authors:  J Charles Taylor
Journal:  Foot Ankle Clin       Date:  2008-03       Impact factor: 1.653

3.  Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

Authors:  Weiguo Xie; Jochen Franke; Cheng Chen; Paul A Grützner; Steffen Schumann; Lutz-P Nolte; Guoyan Zheng
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-07-31       Impact factor: 2.924

4.  Optimization of electronic prescription for parallel external fixator based on genetic algorithm.

Authors:  Xishuai Zhang; Hao Sun; Jianwen Chen; Yue Guo; Yinguang Zhang; Zhenhui Sun; Tao Wang; Mengting Wei; Yan Zhang; Lingling Chen
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-03-18       Impact factor: 2.924

5.  Femur segmentation in DXA imaging using a machine learning decision tree.

Authors:  Dildar Hussain; Mugahed A Al-Antari; Mohammed A Al-Masni; Seung-Moo Han; Tae-Seong Kim
Journal:  J Xray Sci Technol       Date:  2018       Impact factor: 1.535

6.  Mechanical, Anatomical, and Kinematic Axis in TKA: Concepts and Practical Applications.

Authors:  Jeffrey J Cherian; Bhaveen H Kapadia; Samik Banerjee; Julio J Jauregui; Kimona Issa; Michael A Mont
Journal:  Curr Rev Musculoskelet Med       Date:  2014-06

7.  Validation of a statistical shape model-based 2D/3D reconstruction method for determination of cup orientation after THA.

Authors:  G Zheng; J von Recum; L-P Nolte; P A Grützner; S D Steppacher; J Franke
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-07-27       Impact factor: 2.924

8.  Functional and anatomic orientation of the femoral head.

Authors:  David Wright; Cari Whyne; Michael Hardisty; Hans J Kreder; Omri Lubovsky
Journal:  Clin Orthop Relat Res       Date:  2011-01-07       Impact factor: 4.176

9.  Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.

Authors:  Cem M Deniz; Siyuan Xiang; R Spencer Hallyburton; Arakua Welbeck; James S Babb; Stephen Honig; Kyunghyun Cho; Gregory Chang
Journal:  Sci Rep       Date:  2018-11-07       Impact factor: 4.379

10.  A Semi-automatic Diagnosis of Hip Dysplasia on X-Ray Films.

Authors:  Guangyao Yang; Yaoxian Jiang; Tong Liu; Xudong Zhao; Xiaodan Chang; Zhaowen Qiu
Journal:  Front Mol Biosci       Date:  2020-12-17
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