Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach
We present an automated method for extracting anatomical parameters from biplanar radiographs of the spine, which is able to deal with a wide scenario of conditions, including sagittal and coronal deformities, degenerative phenomena as well as images acquired with different fields of view.
The location of 78 landmarks (end plate centers, hip joint centers, and margins of the S1 end plate) was extracted from three-dimensional reconstructions of 493 spines of patients suffering from various disorders, including adolescent idiopathic scoliosis, adult deformities, and spinal stenosis. A fully convolutional neural network featuring an additional differentiable spatial to numerical (DSNT) layer was trained to predict the location of each landmark. The values of some parameters (T4–T12 kyphosis, L1–L5 lordosis, Cobb angle of scoliosis, pelvic incidence, sacral slope, and pelvic tilt) were then calculated based on the landmarks’ locations. A quantitative comparison between the predicted parameters and the ground truth was performed on a set of 50 patients.
The spine shape predicted by the models was perceptually convincing in all cases. All predicted parameters were strongly correlated with the ground truth. However, the standard errors of the estimated parameters ranged from 2.7° (for the pelvic tilt) to 11.5° (for the L1–L5 lordosis).
The proposed method is able to automatically determine the spine shape in biplanar radiographs and calculate anatomical and posture parameters in a wide scenario of clinical conditions with a very good visual performance, despite limitations highlighted by the statistical analysis of the results.
KeywordsDeep learning Spine deformities Automated analysis Coordinate regression Biplanar radiographs
The work has been partially funded by the Italian Ministry of Health (Ricerca Corrente). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this article.
- 5.Cobb J (1948) Outline for the study of scoliosis. Instr Course Lect AAOS 5:261–275Google Scholar
- 9.Sun H, Zhen X, Bailey C, Rasoulinejad P, Yin Y, Li S (2017) Direct estimation of spinal Cobb angles by structured multi-output regression. In: Niethammer M et al (ed) Information processing in medical imaging. IPMI 2017. Lecture notes in computer science, vol 10265. Springer, Cham, pp 529–540Google Scholar
- 10.Wu H, Bailey C, Rasoulinejad P, Li S (2017) Automatic landmark estimation for adolescent idiopathic scoliosis assessment using BoostNet. In: Medical image computing and computer assisted intervention—MICCAI 2017, Quebec City, pp 127–135Google Scholar
- 12.Nibali A, He Z, Morgan S, Prendergast L (2018) Numerical coordinate regression with convolutional neural networks. arXiv preprint arXiv:1801.07372
- 13.Chollet F (2015) Keras: deep learning for humans. https://github.com/keras-team/keras. Accessed 26 Sept 2018
- 14.Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org. Accessed 26 Sept 2018
- 15.Burkardt J (2012). SPLINE—interpolation and approximation of data. https://people.sc.fsu.edu/~jburkardt/cpp_src/spline/spline.html. Accessed 26 Sept 2018
- 18.Jones E, Oliphant T, Peterson P (2014) SciPy: open source scientific tools for Python. https://www.scipy.org. Accessed 26 Sept 2018
- 19.Seabold S, Perktold J (2010) Statsmodels: econometric and statistical modeling with python. In: Proceedings of the 9th Python in Science Conference, vol 57, p 61Google Scholar