A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation
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A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation. / Alukaev, Danis; Kiselev, Semen; Mustafaev, Tamerlan; Ainur, Ahatov; Ibragimov, Bulat; Vrtovec, Tomaž.
In: European Spine Journal, Vol. 31, 2022, p. 2115–2124.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation
AU - Alukaev, Danis
AU - Kiselev, Semen
AU - Mustafaev, Tamerlan
AU - Ainur, Ahatov
AU - Ibragimov, Bulat
AU - Vrtovec, Tomaž
N1 - Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022
Y1 - 2022
N2 - Purpose: To propose a fully automated deep learning (DL) framework for the vertebral morphometry and Cobb angle measurement from three-dimensional (3D) computed tomography (CT) images of the spine, and validate the proposed framework on an external database. Methods: The vertebrae were first localized and segmented in each 3D CT image using a DL architecture based on an ensemble of U-Nets, and then automated vertebral morphometry in the form of vertebral body (VB) and intervertebral disk (IVD) heights, and spinal curvature measurements in the form of coronal and sagittal Cobb angles (thoracic kyphosis and lumbar lordosis) were performed using dedicated machine learning techniques. The framework was trained on 1725 vertebrae from 160 CT images and validated on an external database of 157 vertebrae from 15 CT images. Results: The resulting mean absolute errors (± standard deviation) between the obtained DL and corresponding manual measurements were 1.17 ± 0.40 mm for VB heights, 0.54 ± 0.21 mm for IVD heights, and 3.42 ± 1.36° for coronal and sagittal Cobb angles, with respective maximal absolute errors of 2.51 mm, 1.64 mm, and 5.52°. Linear regression revealed excellent agreement, with Pearson’s correlation coefficient of 0.943, 0.928, and 0.996, respectively. Conclusion: The obtained results are within the range of values, obtained by existing DL approaches without external validation. The results therefore confirm the scalability of the proposed DL framework from the perspective of application to external data, and time and computational resource consumption required for framework training.
AB - Purpose: To propose a fully automated deep learning (DL) framework for the vertebral morphometry and Cobb angle measurement from three-dimensional (3D) computed tomography (CT) images of the spine, and validate the proposed framework on an external database. Methods: The vertebrae were first localized and segmented in each 3D CT image using a DL architecture based on an ensemble of U-Nets, and then automated vertebral morphometry in the form of vertebral body (VB) and intervertebral disk (IVD) heights, and spinal curvature measurements in the form of coronal and sagittal Cobb angles (thoracic kyphosis and lumbar lordosis) were performed using dedicated machine learning techniques. The framework was trained on 1725 vertebrae from 160 CT images and validated on an external database of 157 vertebrae from 15 CT images. Results: The resulting mean absolute errors (± standard deviation) between the obtained DL and corresponding manual measurements were 1.17 ± 0.40 mm for VB heights, 0.54 ± 0.21 mm for IVD heights, and 3.42 ± 1.36° for coronal and sagittal Cobb angles, with respective maximal absolute errors of 2.51 mm, 1.64 mm, and 5.52°. Linear regression revealed excellent agreement, with Pearson’s correlation coefficient of 0.943, 0.928, and 0.996, respectively. Conclusion: The obtained results are within the range of values, obtained by existing DL approaches without external validation. The results therefore confirm the scalability of the proposed DL framework from the perspective of application to external data, and time and computational resource consumption required for framework training.
KW - Artificial intelligence
KW - Cobb angle
KW - Computed tomography
KW - Deep learning
KW - Spine
KW - Vertebral morphometry
UR - http://www.scopus.com/inward/record.url?scp=85130228618&partnerID=8YFLogxK
U2 - 10.1007/s00586-022-07245-4
DO - 10.1007/s00586-022-07245-4
M3 - Journal article
C2 - 35596800
AN - SCOPUS:85130228618
VL - 31
SP - 2115
EP - 2124
JO - European Spine Journal
JF - European Spine Journal
SN - 0940-6719
ER -
ID: 309123826