Literature DB >> 28183004

An integrative framework for 3D cobb angle measurement on CT images.

Xing Huo1, Jie Qing Tan1, Jun Qian2, Li Cheng2, Jue Hua Jing3, Kun Shao1, Bing Nan Li1.   

Abstract

OBJECTIVE: Measuring the Cobb angle on computed tomography (CT) images remains a challenging but requisite task for clinical diagnoses of scoliosis. Traditionally, clinical practitioners resort to manual demarcation, but this approach is inefficient and subjective. Most of the existing computerized algorithms are two-dimensional (2D) and incapable of multi-angle calibration.
METHODS: A novel integrative framework based on curvature features and geometric constraints is proposed to measure three-dimensional (3D)Cobb angles on CT images. This framework enables Cobb angle estimation in stereo and accomplishes the synchronous computation of the Cobb angle in three imaging planes. The whole system was quantitatively evaluated on 22 spine models obtained from the clinic.
RESULTS: The results demonstrate that the integrative framework performs well in clinical Lenke classification and outperforms both the traditional manual method and the 2D digital method as evidenced by high intra-observer and inter-observer reliability (ICC>0.94, SEM 0.9°-1.2° for intra-observer, ICC>0.94, SEM 0.8°-1.2° for inter-observer). This 3D framework is also robust across different models (SE<3°).
CONCLUSIONS: The new integrative framework is able to measure the Cobb angles in three imaging planes simultaneously and is therefore clinically advantageous.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  3D cobb angle measurement; 3D spinal model; Best-fit plane; Computed tomography; Curvature feature

Mesh:

Year:  2017        PMID: 28183004     DOI: 10.1016/j.compbiomed.2017.01.007

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  1 in total

1.  The measurement of Cobb angle based on spine X-ray images using multi-scale convolutional neural network.

Authors:  Jun Liu; Chen Yuan; Xiaoxue Sun; Lechan Sun; Hua Dong; Yun Peng
Journal:  Phys Eng Sci Med       Date:  2021-07-12
  1 in total

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