Automatic analysis of global spinal alignment from simple annotation of vertebral bodies

Doerr, Sophia A.; Silva, Tharindu S. De; Vijayan, Rohan C.; Han, Runze; Uneri, Ali; Ketcha, Michael D.; Zhang, Xiaoxuan; Khanna, N.; Westbroek, Erick M.; Jiang, Bowen; Zygourakis, Corinna C.; Aygün, Nafi; Theodore, Nicholas; Siewerdsen, Jeffrey H.

doi:10.1117/1.jmi.7.3.035001

Cited by 3 publications

(7 citation statements)

References 25 publications

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“…The MThK and LL angles measured in cadaver are summarized in the lower portion of Table 1, where

{{\Delta}}\phi

was −4.2° ± 6.8° for MThK and 1.7° ± 3.5° for LL. The large range (standard deviation) in manual measurements was due to the high level of inter‐reader variability in vertebral endplate definition, consistent with previous studies 15 . No significant difference was detected between manual and SpNorm measurements (in phantom or cadaver) for either MThK or LL.…”

Section: Resultssupporting

confidence: 88%

“…Based on the resulting deformable 3D-2D patient registration, vertebral labels from the preoperative 3D image are registered and projected onto the LF images for GSA analysis. We adapted the Spline-Fit Normals (SpNorm) method 15 to the framework for automatic assessment of spinal curvature. As illustrated in Figure 2, SpNorm is based on a 2D spline fit to the registered labels as a model of overall curvature.Along the length of the spline, normal rays are computed at positions intermediate to specific labels as a surrogate for the angle of vertebral endplates.…”

Section: Assessment Of Spinal Curvaturementioning

confidence: 99%

“…The large range (standard deviation) in manual measurements was due to the high level of inter-reader variability in vertebral endplate definition, consistent with previous studies. 15 No significant difference was TA B L E 1 Comparison of global spinal alignment (GSA) measurements between manual definition and the Spline-Fit Normals (SpNorm) method for various levels of neutral and deformed…”

Section: Gsa Analysismentioning

confidence: 99%

“…We report a novel variation of the multi-scale 3D-2D registration approach proposed by Ketcha et al 33 to support deformable registration of preoperative CT to LF images. Based on the deformably registered vertebral labels, an algorithm for automated GSA analysis 15 was adapted to the context of LF images. Finally, we integrate the globally deformable yet locally rigid 3D-2D patient registration method with the "Known-Component" device registration (KC-Reg) approach 26 for 3D localization of surgical devices in the presence of spinal deformation.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional assessment of GSA involves multiple radiographs and a T‐bar fiducial, which is time‐consuming, subject to interobserver variability, 11 and poorly suited to intraoperative use. A variety of efforts for the computer‐aided assessment of GSA have been reported for (preoperative) diagnostic imaging, including mathematical morphology and deep learning (DL) models 12–15 . Translation of such approaches to intraoperative imaging can provide a valuable assessment of deformity correction in quantitative terms that have shown correlation with clinical outcomes 16 …”

Section: Introductionmentioning

confidence: 99%

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Deformable 3D–2D image registration and analysis of global spinal alignment in long‐length intraoperative spine imaging

et al. 2022

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Background Spinal deformation during surgical intervention (caused by patient positioning and/or the correction of malalignment) confounds conventional navigation due to the assumptions of rigid transformation. Moreover, the ability to accurately quantify spinal alignment in the operating room would provide an assessment of the surgical product via metrics that correlate with clinical outcomes. Purpose A method for deformable 3D–2D registration of preoperative CT to intraoperative long‐length tomosynthesis images is reported for an accurate 3D evaluation of device placement in the presence of spinal deformation and automated evaluation of global spinal alignment (GSA). Methods Long‐length tomosynthesis (“Long Film,” LF) images were acquired using an O‐arm imaging system (Medtronic, Minneapolis USA). A deformable 3D–2D patient registration was developed using multi‐scale masking (proceeding from the full‐length image to local subvolumes about each vertebra) to transform vertebral labels and planning information from preoperative CT to the LF images. Automatic measurement of GSA (main thoracic kyphosis [MThK] and lumbar lordosis [LL]) was obtained using a spline fit to registered labels. The “Known‐Component Registration” method for device registration was adapted to the multi‐scale process for 3D device localization from orthogonal LF images. The multi‐scale framework was evaluated using a deformable spine phantom in which pedicle screws were inserted, and deformations were induced over a range in LL ∼25°–80°. Further validation was carried out in a cadaver study with implanted pedicle screws and a similar range of spinal deformation. The accuracy of patient and device registration was evaluated in terms of 3D translational error and target registration error, respectively, and the accuracies of automatic GSA measurements were compared to manual annotation. Results Phantom studies demonstrated accurate registration via the multi‐scale framework for all vertebral levels in both the neutral and deformed spine: median (interquartile range, IQR) patient registration error was 1.1 mm (0.7–1.9 mm IQR). Automatic measures of MThK and LL agreed with manual delineation within −1.1° ± 2.2° and 0.7° ± 2.0° (mean and standard deviation), respectively. Device registration error was 0.7 mm (0.4–1.0 mm IQR) at the screw tip and 0.9° (1.0°–1.5°) about the screw trajectory. Deformable 3D–2D registration significantly outperformed conventional rigid registration (p < 0.05), which exhibited device registration errors of 2.1 mm (0.8–4.1 mm) and 4.1° (1.2°–9.5°). Cadaver studies verified performance under realistic conditions, demonstrating patient registration error of 1.6 mm (0.9–2.1 mm); MThK within −4.2° ± 6.8° and LL within 1.7° ± 3.5°; and device registration error of 0.8 mm (0.5–1.9 mm) and 0.7° (0.4°–1.2°) for the multi‐scale deformable method, compared to 2.5 mm (1.0–7.9 mm) and 2.3° (1.6°–8.1°) for rigid registration (p < 0.05). Conclusion The deformable 3D–2D registration framework leverages long‐length intraoperat...

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“…The MThK and LL angles measured in cadaver are summarized in the lower portion of Table 1, where

{{\Delta}}\phi

Section: Resultssupporting

confidence: 88%

Section: Assessment Of Spinal Curvaturementioning

confidence: 99%

Section: Gsa Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deformable 3D–2D image registration and analysis of global spinal alignment in long‐length intraoperative spine imaging

et al. 2022

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Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images

Huang

Jones

Zhang

et al. 2022

Computer Methods and Programs in Biomedicine

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3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework

Huang¹,

Uneri²,

Jones³

et al. 2021

Phys. Med. Biol.

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Purpose. Accurate localization and labeling of vertebrae in computed tomography (CT) is an important step toward more quantitative, automated diagnostic analysis and surgical planning. In this paper, we present a framework (called Ortho2D) for vertebral labeling in CT in a manner that is accurate and memory-efficient. Methods. Ortho2D uses two independent faster R-convolutional neural network networks to detect and classify vertebrae in orthogonal (sagittal and coronal) CT slices. The 2D detections are clustered in 3D to localize vertebrae centroids in the volumetric CT and classify the region (cervical, thoracic, lumbar, or sacral) and vertebral level. A post-process sorting method incorporates the confidence in network output to refine classifications and reduce outliers. Ortho2D was evaluated on a publicly available dataset containing 302 normal and pathological spine CT images with and without surgical instrumentation. Labeling accuracy and memory requirements were assessed in comparison to other recently reported methods. The memory efficiency of Ortho2D permitted extension to high-resolution CT to investigate the potential for further boosts to labeling performance. Results. Ortho2D achieved overall vertebrae detection accuracy of 97.1%, region identification accuracy of 94.3%, and individual vertebral level identification accuracy of 91.0%. The framework achieved 95.8% and 83.6% level identification accuracy in images without and with surgical instrumentation, respectively. Ortho2D met or exceeded the performance of previously reported 2D and 3D labeling methods and reduced memory consumption by a factor of ∼50 (at 1 mm voxel size) compared to a 3D U-Net, allowing extension to higher resolution datasets than normally afforded. The accuracy of level identification increased from 80.1% (for standard/low resolution CT) to 95.1% (for high-resolution CT). Conclusions. The Ortho2D method achieved vertebrae labeling performance that is comparable to other recently reported methods with significant reduction in memory consumption, permitting further performance boosts via application to high-resolution CT.

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Automatic analysis of global spinal alignment from simple annotation of vertebral bodies

Cited by 3 publications

References 25 publications

Deformable 3D–2D image registration and analysis of global spinal alignment in long‐length intraoperative spine imaging

Deformable 3D–2D image registration and analysis of global spinal alignment in long‐length intraoperative spine imaging

Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images

3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework

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