Lumbar spine posterior corner detection in X-rays using Haar-based features

Ebrahimi, Shahin; Angelini, Elsa D.; Gajny, Laurent; Skalli, Wafa

doi:10.1109/isbi.2016.7493239

Cited by 8 publications

(10 citation statements)

References 15 publications

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“…The initial solution was automatically calculated through statistical inference [20,21] and image analysis [22,23]. The 3D reconstructed vertebrae thus obtained were retro-projected ( Figure 2) on the frontal radiography.…”

Section: Quasi-automatic 3d Reconstructionmentioning

confidence: 99%

Quasi-automatic early detection of progressive idiopathic scoliosis from biplanar radiography: a preliminary validation

et al. 2019

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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 included (Age: 10-15, Cobb: 16° ± 4°). Patients underwent low-dose biplanar x-rays and a novel fast method for 3D reconstruction of the spine was performed. They were followed until skeletal maturity (stable patients) or brace prescription (progressive patients). The severity index was calculated at the first exam, based on 3D parameters of the scoliotic curve, and it was compared with the patient's final outcome (progressive or stable). Three operators have repeated the 3D reconstruction twice for a subset of 30 patients to assess reproducibility (through Cohen's kappa and intraclass correlation coefficient). Results 85% of the patients were correctly classified as stable or progressive by the severity index, with a sensitivity of 92% and specificity of 74%. Substantial intra-operator agreement and good inter-operator agreement were observed, with 80% of the progressive patients correctly detected at the first exam. The novel severity index assessment took less than 4 minutes of operator time. Conclusions The fast and semi-automatic method for 3D reconstruction developed in this work allowed for a fast and reliable calculation of the severity index. The method is fast and user friendly. Once extensively validated, this severity index could allow very early initiation of conservative treatment for progressive patients, thus increasing treatment efficacy and therefore reducing the need for corrective surgery.

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Section: Quasi-automatic 3d Reconstructionmentioning

confidence: 99%

Quasi-automatic early detection of progressive idiopathic scoliosis from biplanar radiography: a preliminary validation

et al. 2019

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“…The regression method is iterative and it self-improves the model as the algorithm automatically decides to which vertebrae the digitalized endplates belong to. Moreover, lumbar and cervical vertebral corners and visible thoracic endplates were detected automatically and accurately using image processing [25] and machine learning (see Fig.2). The obtained pieces of information enabled to compute new predictors that again improved the model.…”

Section: Estimation Of the Vertebrae Position And Orientationmentioning

confidence: 99%

Quasi-automatic 3D reconstruction of the full spine from low-dose biplanar X-rays based on statistical inferences and image analysis

et al. 2018

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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 reconstruction time is more than 10 minutes per patient. Methods: The proposed method of 3D reconstruction of the spine (C3-L5) relies first on a new manual input strategy designed to fit clinicians' skills. Then, a parametric model of the spine is computed using statistical inferences, image analysis techniques and fast manual rigid registration. Results: An agreement study with the clinically used method on a cohort of 57 adolescent scoliotic subjects has shown that both methods have similar performance on vertebral body position and axial rotation (null bias in both cases and standard deviation of signed differences of 1mm and 3.5° around respectively). In average, the solution could be computed in less than 5 minutes of operator time, even for severe scoliosis. Conclusions: The proposed method allows fast and accurate 3D reconstruction of the spine for wide clinical applications and represents a significant step toward full automatization of 3D reconstruction of the spine. Moreover, it is to the best of our knowledge the first method including also the cervical spine.

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“…Following the method and parameters described in the study of (Ebrahimi et al, 2016), we convert the edge map into a set of connected edge segments using the polyline simplification method introduced in (Douglas and Peuker, 1973) and discard points placed along flat edges. This leads to a subset of edge points that are considered as corner point candidates (magenta points in Figure 7(f&g)).…”

Section: Corner Point Candidate Extractionmentioning

confidence: 99%

“…Two features based on Haar-like filtering were introduced in (Ebrahimi et al, 2016). In the current study, we add 16 Haar-like features, to improve sensitivity to corner orientation and local appearance.…”

Section: Haar-like Featuresmentioning

confidence: 99%

“…In this study, aiming for a more generic solution for X-ray imaging, we elaborate on the previous work of (Ebrahimi et al, 2016) which introduced a competitive posterior lumbar corner detection and categorization framework based on Haar-like features. Our proposed method combines shape modelling, dedicated visual features, and random forest classification for vertebrae corner localization with the aim to become independent of detailed manual landmark selection while maintaining accurate landmark localization even in pathological cases.…”

Section: Introductionmentioning

confidence: 99%

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Vertebral corners detection on sagittal X-rays based on shape modelling, random forest classifiers and dedicated visual features

Ebrahimi

Gajny

Skalli

et al. 2018

Computer Methods in Biomechanics and Biomedical Engineering: Im

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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 limit manual input, while demonstrating precise vertebrae corners positioning and shape parameter measurements from sagittal radiographs of the cervical and lumbar regions, exploiting novel dedicated visual features and specialized classifiers. A database of manually annotated X-ray images is used to train specialized Random Forest classifiers for each spine regions and corner types. An original combination of local gradient characteristics, Haarlike features, and contextual features based on patch intensity and contrast is used as visual features. The proposed method is evaluated on 49 sagittal X-rays of asymptomatic and pathological subjects, from multiple imaging sites, and with a large age range (6-69 years old). Performance is first evaluated for positioning a 2D spine shape model, where precisely detected corners enable to adjust the model via an original multilinear statistical regression. Root-mean square errors (RMSE) of corners localization and vertebra orientations are reported, demonstrating state-of-the-art precision compared to existing methods, but with minimal manual input. The method is then evaluated for the extraction of additional vertebrae shape characteristics, such as centre positioning, endplate centres positioning and endplate length measures, rarely studied in previous literature. The proposed method enables, with minimal initialization, fast and precise individual vertebrae delineations on sagittal radiographs on normal and pathological cases, with a level of precision and robustness required for objective support for diagnosis and therapy decision making.

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Lumbar spine posterior corner detection in X-rays using Haar-based features

Cited by 8 publications

References 15 publications

Quasi-automatic early detection of progressive idiopathic scoliosis from biplanar radiography: a preliminary validation

Quasi-automatic early detection of progressive idiopathic scoliosis from biplanar radiography: a preliminary validation

Quasi-automatic 3D reconstruction of the full spine from low-dose biplanar X-rays based on statistical inferences and image analysis

Vertebral corners detection on sagittal X-rays based on shape modelling, random forest classifiers and dedicated visual features

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