Deep Learning Based 3D Reconstruction of the Spine from Low Dose Biplanar Radiographs
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-55315-8_17
Reference12 articles.
1. Humbert, L., et al.: 3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences. Med. Eng. Phys. 31, 681–687 (2009)
2. Nault, M.-L., et al.: A predictive model of progression for adolescent idiopathic scoliosis based on 3D spine parameters at first visit. Spine (Phila Pa 1976) 45(9), 605–611 (2020)
3. Carreau, J., et al.: Computer-generated, three-dimensional spine model from biplanar radiographs: a validity study in idiopathic scoliosis curves greater than 50 degrees. Spine Deform. 2, 81–88 (2014)
4. Gajny, L., et al.: Quasi-automatic 3D reconstruction of the full spine from low-dose biplanar X-rays based on statistical inferences and image analysis. Eur. Spine J. 28, 658–664 (2019)
5. Vergari, C., et al.: Effect of curve location on the severity index for adolescent idiopathic scoliosis: a longitudinal cohort study. Eur. Radiol. 31, 8488–8497 (2021)
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