Automatic Quantification of Spinal Curvature in Scoliotic Radiograph using Image Processing

Anitha, H; Prabhu, K. Gopalakrishna

doi:10.1007/s10916-011-9654-9

Cited by 49 publications

(15 citation statements)

References 15 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In previous studies, the reliabilities of computer-aided methods [ 5 , 6 , 8 ] were less than 3° for the Cobb angle measurements. For the model radiographs, the results of the proposed method are comparable to those reported in previous studies (i.e., ≤3°).…”

Section: Resultsmentioning

confidence: 99%

“…In this method, false detection might occur if a vertebra tilts by more than 45°. To facilitate the identification of end-vertebrae and to eliminate observer variations, Anitha and Prabhu [ 6 ] proposed listing the horizontal inclinations of all vertebrae in terms of slopes by using active contour models and morphological operators. Okashi et al [ 7 ] developed an automatic method for spine segmentation and curvature quantification from poor-quality X-ray images of mice.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

Zhang

et al. 2017

International Journal of Biomedical Imaging

View full text Add to dashboard Cite

Objective To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment. Methods A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs. Results For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°. Conclusion The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN. Significance Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

Zhang

et al. 2017

International Journal of Biomedical Imaging

View full text Add to dashboard Cite

show abstract

“…Evaluate the resulting parametric function over the vertical span of the defined points, to yield a smooth left-hand curve. 4. Repeat the process with right-edge points to yield a smooth right-hand curve, 5.…”

Section: Extracting the Cscmentioning

confidence: 99%

“…This paper seeks to advance a variation on the widely practiced manual methods of Cobb 1 and Ferguson, 2 and recently proposed digital approaches [3][4][5][6][7] for characterizing spinal deformity in coronal images of the spine. These methods share a common trait: they rely on discernment, either by human visual identification or through computer enhancement, of the boundaries of individual vertebrae of the spine.…”

Section: Introductionmentioning

confidence: 99%

“…The manual methods of Cobb and Ferguson, respectively, target the endplate surfaces and vertebra centers, and from these vertebra-specific features, infer characteristic angles for the curvature. Zhang et al, 3 Anitha and Prabhu, 4 Abuzaghleh and Barkana, 5 and Kundu et al 6 describe computer-aided approaches for isolating the vertebra features and computing angles, but all hinge on the application of Hough transform 7 and edge detection for defining the vertebra boundaries. Dewaele 8 measures curvature by deriving a geometric mapping to a normal reference spine image, but also relies on pre-processing to isolate the vertebra as closed shapes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Contour and Angle-Function Based Scoliosis Monitoring: Relaxing the Requirement on Image Quality in the Measurement of Spinal Curvature

Bonanni

2017

Int J Spine Surg

View full text Add to dashboard Cite

Because the spine contour is discernible even in low quality images where vertebral endplates may be obscured or poorly contrasted from surrounding tissue, the approach offers improved reliability, applicability across imaging modalities, and, in the case of x-rays, the possibility of a reduced radiation dose. Moreover, since it relies on larger image features and exploits the continuity of the spine, the contour-based approach is expected to reduce the variability associated with Cobb angle measurement.

show abstract

Institution‐wide shape analysis of 3D spinal curvature and global alignment parameters

Iriondo

Mehany

Shah

et al. 2021

Journal Orthopaedic Research

View full text Add to dashboard Cite

The spine is an articulated, 3D structure with 6 degrees of translational and rotational freedom. Clinical studies have shown spinal deformities are associated with pain and functional disability in both adult and pediatric populations. Clinical decision making relies on accurate characterization of the spinal deformity and monitoring of its progression over time. However, Cobb angle measurements are time‐consuming, are limited by interobserver variability, and represent a simplified 2D view of a 3D structure. Instead, spine deformities can be described by 3D shape parameters, addressing the limitations of current measurement methods. To this end, we develop and validate a deep learning algorithm to automatically extract the vertebral midline (from the upper endplate of S1 to the lower endplate of C7) for frontal and lateral radiographs. Our results demonstrate robust performance across datasets and patient populations. Approximations of 3D spines are reconstructed from the unit normalized midline curves of 20,118 pairs of full spine radiographs belonging to 15,378 patients acquired at our institution between 2008 and 2020. The resulting 3D dataset is used to describe global imbalance parameters in the patient population and to build a statistical shape model to describe global spine shape variations in preoperative deformity patients via eight interpretable shape parameters. The developed method can identify patient subgroups with similar shape characteristics without relying on an existing shape classification system.

show abstract

Automatic Quantification of Spinal Curvature in Scoliotic Radiograph using Image Processing

Cited by 49 publications

References 15 publications

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network

Contour and Angle-Function Based Scoliosis Monitoring: Relaxing the Requirement on Image Quality in the Measurement of Spinal Curvature

Institution‐wide shape analysis of 3D spinal curvature and global alignment parameters

Contact Info

Product

Resources

About