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Purpose <p>Scoliosis is a prevalent spine deformity that impacts millions of children globally. The Cobb angle, a crucial and widely-accepted metric, serves as the “gold standard” for assessing scoliosis in patients. However, the traditional manual measurement of spine curvature is time-consuming and labor-intensive. It also comes with issues like intra - and inter-observer variations. Moreover, accurately and robustly evaluating Cobb angles is extremely challenging. This is because it necessitates the correct identification of all the required vertebrae in both the anterior-posterior (AP) and lateral (LAT) views of full-spine digital radiography (DR).</p> Methods <p>To solve these challenges, a deep learning-based framework is developed to fully automatically measure patient Cobb angels from full-spine DR of both AP and LAT views. First, a deep learning network was used to distinguish AP and LAT views. Then the region of interest (ROI) of the whole spine was located and extracted. Subsequently, a detection network was applied to detect and identify the boundaries and locations, the types, and the four corner points of each spinal vertebra. Finally, the Cobb angles was measured automatically. When taking into account the location, recognition, and key points detection of spinal vertebrae, YOLOv8 architecture with CBAM module was adopted as the backbone.</p> Results <p>A total of 1,163 AP view and 1,378 LAT view DR images were used to train and evaluate the models. Experimental results in the evaluation testing showed a mean Cobb angle error of 2.56° for AP view and 2.498° for LAT view DR images. The intra-class correlation coefficient (ICC) with 95% confidence interval (CI) was 0.956 (0.932, 0.972) for AP view and 0.925 (0.888, 0.952) for LAT view. The Pearson correlation coefficient was 0.961 for AP view and 0.930 for LAT view. In the comprehensive reader study, for the major curve, a mean Cobb angle error of 3.918°, an ICC of 0.943 (0.912, 0.965), and a high correlation coefficient of 0.960 were obtained.</p> Conclusion <p>The results showed that the proposed framework had a significant accuracy and consistency advantage in measuring Cobb angle, which not only validated the effectiveness of the algorithm, but also provided strong support for the diagnosis of clinicians.</p>

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A fully automatic Cobb angle measurement framework of full-spine DR images based on deep learning

  • 期刊论文

  • 发表日期：

    2025年5月6日

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  • Huijie Wu,
  • Shasha Zheng,
  • Wang Du,
  • Jingchao Zhang,
  • Zhenzhen Wang,
  • Lixue Lu,
  • Chengxi Yan,
  • Xue Hou,
  • Minglei Yang,
  • Shuangfeng Dai

摘要

Purpose

Scoliosis is a prevalent spine deformity that impacts millions of children globally. The Cobb angle, a crucial and widely-accepted metric, serves as the “gold standard” for assessing scoliosis in patients. However, the traditional manual measurement of spine curvature is time-consuming and labor-intensive. It also comes with issues like intra - and inter-observer variations. Moreover, accurately and robustly evaluating Cobb angles is extremely challenging. This is because it necessitates the correct identification of all the required vertebrae in both the anterior-posterior (AP) and lateral (LAT) views of full-spine digital radiography (DR).

Methods

To solve these challenges, a deep learning-based framework is developed to fully automatically measure patient Cobb angels from full-spine DR of both AP and LAT views. First, a deep learning network was used to distinguish AP and LAT views. Then the region of interest (ROI) of the whole spine was located and extracted. Subsequently, a detection network was applied to detect and identify the boundaries and locations, the types, and the four corner points of each spinal vertebra. Finally, the Cobb angles was measured automatically. When taking into account the location, recognition, and key points detection of spinal vertebrae, YOLOv8 architecture with CBAM module was adopted as the backbone.

Results

A total of 1,163 AP view and 1,378 LAT view DR images were used to train and evaluate the models. Experimental results in the evaluation testing showed a mean Cobb angle error of 2.56° for AP view and 2.498° for LAT view DR images. The intra-class correlation coefficient (ICC) with 95% confidence interval (CI) was 0.956 (0.932, 0.972) for AP view and 0.925 (0.888, 0.952) for LAT view. The Pearson correlation coefficient was 0.961 for AP view and 0.930 for LAT view. In the comprehensive reader study, for the major curve, a mean Cobb angle error of 3.918°, an ICC of 0.943 (0.912, 0.965), and a high correlation coefficient of 0.960 were obtained.

Conclusion

The results showed that the proposed framework had a significant accuracy and consistency advantage in measuring Cobb angle, which not only validated the effectiveness of the algorithm, but also provided strong support for the diagnosis of clinicians.