Automatic Cobb angle measurement method based on vertebra segmentation by deep learning

Zhao, Yang; Zhang, Junhua; Li, Hongjian; Gu, Xiaoying; Li, Zonggui; Zhang, Shuai

doi:10.1007/s11517-022-02563-7

Cited by 21 publications

(19 citation statements)

References 30 publications

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“…The proposed method effectively tackles the issue of improper edges in the spinal vertebral mask by implementing the center curve smoothing technique and thus reducing observers’ variability. The algorithm estimates Cobb angles with a minimal bias of only ± 3 degrees, which aligns with the findings of recent studies on automatic Cobb angle measurement [ 31 , 32 ]. Notably, the proposed LCM-based method does not rely on pretrained models or weights, eliminating the need for time-consuming training, validation, and testing steps typically seen in CNN-based methods [ 32 ].…”

Section: Discussionsupporting

confidence: 87%

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement

Sikkandar,

Alhashim,

Alassaf

et al. 2024

PLoS ONE

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Scoliosis is a medical condition in which a person’s spine has an abnormal curvature and Cobb angle is a measurement used to evaluate the severity of a spinal curvature. Presently, automatic Existing Cobb angle measurement techniques require huge dataset, time-consuming, and needs significant effort. So, it is important to develop an unsupervised method for the measurement of Cobb angle with good accuracy. In this work, an unsupervised local center of mass (LCM) technique is proposed to segment the spine region and further novel Cobb angle measurement method is proposed for accurate measurement. Validation of the proposed method was carried out on 2D X-ray images from the Saudi Arabian population. Segmentation results were compared with GMM-Based Hidden Markov Random Field (GMM-HMRF) segmentation method based on sensitivity, specificity, and dice score. Based on the findings, it can be observed that our proposed segmentation method provides an overall accuracy of 97.3% whereas GMM-HMRF has an accuracy of 89.19%. Also, the proposed method has a higher dice score of 0.54 compared to GMM-HMRF. To further evaluate the effectiveness of the approach in the Cobb angle measurement, the results were compared with Senior Scoliosis Surgeon at Multispecialty Hospital in Saudi Arabia. The findings indicated that the segmentation of the scoliotic spine was nearly flawless, and the Cobb angle measurements obtained through manual examination by the expert and the algorithm were nearly identical, with a discrepancy of only ± 3 degrees. Our proposed method can pave the way for accurate spinal segmentation and Cobb angle measurement among scoliosis patients by reducing observers’ variability.

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Section: Discussionsupporting

confidence: 87%

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement

Sikkandar,

Alhashim,

Alassaf

et al. 2024

PLoS ONE

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“…A limitation of this study is that there were not as many severe curves included in the Cobb angle measurement test set. Our method reports the curve severity distribution and measures accurately on curves >20°, unlike Horng et al and Zhao et al [6] , [8] . However, while the algorithm did achieve 100% of A-Cobb measurements within clinical acceptance for severe curves, 12 datapoints is not enough to confidently conclude that the algorithm performs well on curves \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\ge 45^{\circ }$\end{document} .…”

Section: Discussionmentioning

confidence: 99%

“…Two other relevant papers reported on an approach that involved CNN segmentation of spinal features to derive automatic Cobb angle measurement [6] , [8] . However, the segmentation targets for our method differ from the other papers.…”

Section: Discussionmentioning

confidence: 99%

“…Because of the outlined benefits, some groups have tackled the problem of automating Cobb angle measurement on PA radiographs. Many of them use a convolutional neural network (CNN) based approach and achieved accurate results [6] , [7] , [8] . However, these methods suffer from either testing on only a small subset in the wide range of curve severities present in the AIS population, or offering little interpretability on how the method measured the Cobb angles.…”

Section: Introductionmentioning

confidence: 99%

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Applying Machine Learning and Point-Set Registration to Automatically Measure the Severity of Spinal Curvature on Radiographs

Wong,

Reformat,

Lou

2024

IEEE J. Transl. Eng. Health Med.

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Objective: Measuring the severity of the lateral spinal curvature, or Cobb angle, is critical for monitoring and making treatment decisions for children with adolescent idiopathic scoliosis (AIS). However, manual measurement is time-consuming and subject to human error. Therefore, clinicians seek an automated measurement method to streamline workflow and improve accuracy. This paper reports on a novel machine learning algorithm of cascaded convolutional neural networks (CNN) to measure the Cobb angle on spinal radiographs automatically. Methods: The developed method consisted of spinal column segmentation using a CNN, vertebra localization and segmentation using iterative vertebra body location coupled with another CNN, point-set registration to correct vertebra segmentations, and Cobb angle measurement using the final segmentations. Measurement performance was evaluated with the circular mean absolute error (CMAE) and percentage within clinical acceptance ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\le 5^{\circ }$\end{document} ) between automatic and manual measurements. Analysis was separated by curve severity to identify any potential systematic biases using independent samples Student’s t-tests. Results: The method detected 346 of the 352 manually measured Cobb angles (98%), with a CMAE of 2.8° and 91% of measurements within the 5° clinical acceptance. No statistically significant differences were found between the CMAEs of mild ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $ < 25^{\circ }$\end{document} ), moderate (25°-45°), and severe ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\ge 45^{\circ }$\end{document} ) groups. The average measurement time per radiograph was 17.7±10.2s, improving upon the estimated average of 30s it takes an experienced rater to measure. Additionally, the algorithm outputs segmentations with the measurement, allowing clinicians to interpret measurement results. Discussion/Conclusion: The developed method measured Cobb angles on radiographs automatically with high accuracy, quick measurement time, and interpretability, suggesting clinical feasibility.

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“…However, these methods require the manual selection of the appropriate end vertebrae and endplates by an operator. In this context, there has been a recent marked increase in reports on arti cial intelligence (AI) algorithms for fully automated measurement of CA using convolutional neural networks (CNN) [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. The measurement error of the CA in these reports ranged from 1.9° to 9.9°, which is similar to or lower than that in the manual and computer-assisted manual methods.…”

Section: Introductionmentioning

confidence: 99%

Comparative Accuracy of Three Artificial Intelligence Algorithms for Automatic Cobb Angle Measurement in Scoliosis: An Analysis Using Disease-specific Teaching Data

Kato,

Maeda,

Nagura

et al. 2024

Preprint

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Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), affect many patients. The measurement of the Cobb angle on coronal radiographs is essential for their diagnosis and treatment planning. To enhance the precision of Cobb angle measurements for both AIS and ASD, we developed three distinct artificial intelligence (AI) algorithms: AIS/ASD-trained AI (trained with both AIS and ASD cases); AIS-trained AI (trained solely on AIS cases); ASD-trained AI (trained solely on ASD cases). We used 1,612 whole-spine radiographs, including 1,029 AIS and 583 ASD cases with variable postures, as teaching data. We measured the major and two minor curves. To assess the accuracy, we used 285 radiographs (159 AIS and 126 ASD) as a test set and calculated the mean absolute error (MAE) and intraclass correlation coefficient (ICC) between each AI algorithm and the average of manual measurements by four spine experts. The AIS/ASD-trained AI showed the highest accuracy among the three AI algorithms. This result suggested that learning across multiple diseases rather than disease-specific training may be an efficient AI learning method. The presented AI algorithm has the potential to reduce errors in Cobb angle measurements and improve the quality of clinical practice.

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Automatic Cobb angle measurement method based on vertebra segmentation by deep learning

Cited by 21 publications

References 30 publications

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement

Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement

Applying Machine Learning and Point-Set Registration to Automatically Measure the Severity of Spinal Curvature on Radiographs

Comparative Accuracy of Three Artificial Intelligence Algorithms for Automatic Cobb Angle Measurement in Scoliosis: An Analysis Using Disease-specific Teaching Data

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