Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting

Aubert, B.; Vázquez, Carlos; Cresson, Thierry; Parent, Stefan; Guise, Jacques A. de

doi:10.1109/tmi.2019.2914400

Cited by 47 publications

(30 citation statements)

References 47 publications

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“…Mean Dice values of each class, all above 0.87, compare well with [18] where they studied only frontal views using a smaller dataset (35 images). Our mean RMSE (SD) 1.11 mm ± 0.67 mm on frontal views and 1.92mm ± 1.38 mm on sagittal views are also comparable with the reconstruction results in [11], with 1.6 (1.3) mm for mean 3D Euclidean distance (SD) errors of VBC landmark locations.…”

Section: Discussionsupporting

confidence: 82%

“…The first category predicts spinal landmarks [9,17] or segments vertebrae [18] to measure directly some clinical parameters, mainly the Cobb angle, on landmarks or masks. The second category predicts landmarks and uses them to initialize a statistical shape model [11]. This enables full spine reconstruction, and the evaluation of more clinical parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, research works have aimed to automated 3D spine reconstruction [8,9,10,11]. In general, the first step is to initialize a simplified statistical shape model [6] thanks to several landmarks such as the spinal midline, passing through endplates or vertebral body centers.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], they use manual inputs while in [9] they rely on a fully convolutional neural network (CNN) combined with an additional differentiable spatial to numerical (DSNT) layer to predict the locations of landmarks. In [11], a coarse localization based on the image intensity distribution of columns and rows is used, followed by a detailed statistical shape model (SSM) for landmarks position prediction and CNN patch-based regression models to correct the landmarks.…”

Section: Introductionmentioning

confidence: 99%

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Automated Spinal Midline Delineation on Biplanar X-Rays Using Mask R-CNN

et al. 2019

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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 body centers (VBCs) and endplate midpoints, is required. This paper presents an automated spinal midline delineation method on frontal and sagittal views by using Mask R-CNN. The network detects all vertebrae from C7 to L5, followed by vertebrae segmentation and classification at the same time. After postprocessing to discard outliers, the vertebrae mask centers were regarded as VBCs to get the spine midline by polynomial fitting. Evaluation of the spinal midline on 136 images used root mean square error (RMSE) with respect to manual ground-truth. The RMSE ± standard error values of predicted spinal midlines (C7-L5) were 1.11 mm ± 0.67 mm on frontal views and 1.92mm ± 1.38 mm on sagittal views. The proposed method is capable of delineating spinal midlines on patients with different spine deformity degrees.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Spinal Midline Delineation on Biplanar X-Rays Using Mask R-CNN

et al. 2019

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“…CNN is essentially a multi-layer perceptron, and the key to its success lies in its local connection and sharing of weights. On the one hand, reducing the number of weights makes the network easy to optimize, and on the other hand, reduces the risk of overfitting [19]. CNN is a kind of neural network.…”

Section: B Application Of Cnn In Medicinementioning

confidence: 99%

Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Han

et al. 2020

IEEE Access

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Three‐dimensional ultrasonography could be a potential non‐ionizing tool to evaluate vertebral rotation of subjects with adolescent idiopathic scoliosis

et al. 2023

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BackgroundThree‐dimensional (3D) ultrasonography is nonionizing and has been demonstrated to be a reliable tool for scoliosis assessment, including coronal and sagittal curvatures. It shows a great potential for axial vertebral rotation (AVR) evaluation, yet its validity and reliability need to be further demonstrated.Materials and MethodsTwenty patients with adolescent idiopathic scoliosis (AIS) (coronal Cobb: 26.6 ± 9.1°) received 3D ultrasound scan for twice, 10 were scanned by the same operator, and the other 10 by different operators. EOS Bi‐planar x‐rays and 3D scan were conducted on another 29 patients on the same day. Two experienced 3D ultrasonographic researchers, with different experiences on AVR measurement, evaluated the 3D ultrasonographic AVR of the 29 patients (55 curves; coronal Cobb angle: 26.9 ± 11.3°). The gold standard AVR was determined from the 3D reconstruction of coronal and sagittal EOS radiographs. Intra‐class correlation coefficients (ICCs), mean absolute difference (MAD), standard error measurements (SEM), and Bland–Altman's bias were reported to evaluate the intra‐operator and inter‐operator/rater reliabilities of 3D ultrasonography. The reliability of 3D ultrasonographic AVR measurements was further validated using inter‐method with that of EOS.ResultsICCs for intra‐operator and inter‐operator/rater reliability assessment were all greater than 0.95. MAD, SEM, and bias for the 3D ultrasonographic AVRs were no more than 2.2°, 2.0°, and 0.5°, respectively. AVRs between both modalities were strongly correlated (R2 = 0.901) and not significantly different (p = 0.205). Bland–Altman plot also shows that the bias was less than 1°, with no proportional bias between the difference and mean of expected and radiographic Cobb angles.ConclusionThis study demonstrates that 3D ultrasonography is valid and reliable to evaluate AVR in AIS patients. 3D ultrasonography can be a potential tool for screening and following up subjects with AIS and evaluating the effectiveness of nonsurgical treatments.

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Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting

Cited by 47 publications

References 47 publications

Automated Spinal Midline Delineation on Biplanar X-Rays Using Mask R-CNN

Automated Spinal Midline Delineation on Biplanar X-Rays Using Mask R-CNN

Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Three‐dimensional ultrasonography could be a potential non‐ionizing tool to evaluate vertebral rotation of subjects with adolescent idiopathic scoliosis

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