Vertebral rotation estimation from frontal X-rays using a quasi-automated pedicle detection method
TypeArticles dans des revues avec comité de lecture
Purpose Measurement of vertebral axial rotation (VAR) is relevant for the assessment of scoliosis. Stokes method allows estimating VAR in frontal X-rays from the relative position of the pedicles and the vertebral body. This method requires identifying these landmarks for each vertebral level, which is time-consuming. In this work, a quasi-automated method for pedicle detection and VAR estimation was proposed. Method A total of 149 healthy and adolescent idiopathic scoliotic (AIS) subjects were included in this retrospective study. Their frontal X-rays were collected from multiple sites and manually annotated to identify the spinal midline and pedicle positions. Then, an automated pedicle detector was developed based on image analysis, machine learning and fast manual identification of a few landmarks. VARs were calculated using the Stokes method in a validation dataset of 11 healthy (age 6–33 years) and 46 AIS subjects (age 6–16 years, Cobb 10°–46°), both from detected pedicles and those manually annotated to compare them. Sensitivity of pedicle location to the manual inputs was quantified on 20 scoliotic subjects, using 10 perturbed versions of the manual inputs. Results Pedicles centers were localized with a precision of 84% and mean difference of 1.2 ± 1.2 mm, when comparing with manual identification. Comparison of VAR values between automated and manual pedicle localization yielded a signed difference of − 0.2 ± 3.4°. The uncertainty on pedicle location was smaller than 2 mm along each image axis. Conclusion The proposed method allowed calculating VAR values in frontal radiographs with minimal user intervention and robust quasi-automated pedicle localization.
Fichier(s) constituant cette publication
Cette publication figure dans le(s) laboratoire(s) suivant(s)
Visualiser des documents liés par titre, auteur, créateur et sujet.
Quasi-automatic 3D reconstruction of the full spine from low-dose biplanar X-rays based on statistical inferences and image analysis GAJNY, Laurent; EBRAHIMI, Shahin; VERGARI, Claudio; ANGELINI, Elsa; SKALLI, Wafa (Springer Nature, 2018)Purpose: To design a quasi-automated three-dimensional reconstruction method of the spine from biplanar X-rays as the daily used method in clinical routine is based on manual adjustments of a trained operator and the ...
EBRAHIMI, Shahin; ANGELINI, Elsa; GAJNY, Laurent; SKALLI, Wafa (IEEE, 2016)3D reconstruction of the spine using biplanar X-rays remains approximate and requires human-machine interactions to adjust the position of important features such as vertebral corners and endplates. The purpose of this ...
Vertebral corners detection on sagittal X-rays based on shape modelling, random forest classifiers and dedicated visual features EBRAHIMI, Shahin; GAJNY, Laurent; SKALLI, Wafa; ANGELINI, Elsa (Informa UK Limited, 2018)Quantitative measurements of spine shape parameters on planar X-ray images is critical for clinical applications but remains tedious and with no fully-automated solution demonstrated on the whole spine. This study aims to ...
YANG, Zixin; SKALLI, Wafa; VERGARI, Claudio; ANGELINI, Elsa; GAJNY, Laurent (Springer International Publishing, 2019)Manually annotating medical images with few landmarks to initialize 3D shape models is a common practice. For instance, when reconstructing the 3D spine from biplanar X-rays, the spinal midline, passing through vertebrae ...
Quasi-automatic early detection of progressive idiopathic scoliosis from biplanar radiography: a preliminary validation VERGARI, Claudio; GAJNY, Laurent; COURTOIS, Isabelle; EBERMEYER, Eric; ABELIN-GENEVOIS, Kariman; KIM, Youngwoo; LANGLAIS, Tristan; VIALLE, Raphaël; ASSI, Ayman; GHANEM, Ismat; DUBOUSSET, Jean; SKALLI, Wafa (2019)Purpose To validate the predictive power and reliability of a novel quasi-automatic method to calculate the severity index of adolescent idiopathic scoliosis (AIS). Methods Fifty-five AIS patients were prospectively ...