Cellular automata segmentation of the boundary between the compacta of vertebral bodies and surrounding structures

Egger, Jan; Nimsky, Christopher

doi:10.1117/12.2209039

Cited by 2 publications

(1 citation statement)

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“…In this contribution, an interactive version of the cellular automata algorithm called GrowCut was applied to the segmentation of vertebral bodies in 3D (preliminary results have been presented at the spine congress of the DGNC in Frankfurt, Germany 29 and as SPIE poster 30 ). In a nutshell, it was discovered that a semi-automatic segmentation with GrowCut can achieve a similar accuracy as pure manual slice-by-slice segmentations while contemporaneously reducing the segmentation time.…”

Section: Introductionmentioning

confidence: 99%

Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application

2017

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Objectives:Spinal diseases are very common; for example, the risk of osteoporotic fracture is 40% for White women and 13% for White men in the United States during their lifetime. Hence, the total number of surgical spinal treatments is on the rise with the aging population, and accurate diagnosis is of great importance to avoid complications and a reappearance of the symptoms. Imaging and analysis of a vertebral column is an exhausting task that can lead to wrong interpretations. The overall goal of this contribution is to study a cellular automata-based approach for the segmentation of vertebral bodies between the compacta and surrounding structures yielding to time savings and reducing interpretation errors.Methods:To obtain the ground truth, T2-weighted magnetic resonance imaging acquisitions of the spine were segmented in a slice-by-slice procedure by several neurosurgeons. Subsequently, the same vertebral bodies have been segmented by a physician using the cellular automata approach GrowCut.Results:Manual and GrowCut segmentations have been evaluated against each other via the Dice Score and the Hausdorff distance resulting in 82.99% ± 5.03% and 18.91 ± 7.2 voxel, respectively. Moreover, the times have been determined during the slice-by-slice and the GrowCut course of actions, indicating a significantly reduced segmentation time (5.77 ± 0.73 min) of the algorithmic approach.Conclusion:In this contribution, we used the GrowCut segmentation algorithm publicly available in three-dimensional Slicer for three-dimensional segmentation of vertebral bodies. To the best of our knowledge, this is the first time that the GrowCut method has been studied for the usage of vertebral body segmentation. In brief, we found that the GrowCut segmentation times were consistently less than the manual segmentation times. Hence, GrowCut provides an alternative to a manual slice-by-slice segmentation process.

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Section: Introductionmentioning

confidence: 99%

Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application

2017

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Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations

Guy

Weiss

Che

et al. 2019

Advances in Radiation Oncology

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PurposeTo evaluate accuracy for 2 deformable image registration methods (in-house B-spline and MIM freeform) using image pairs exhibiting changes in patient orientation and lung volume and to assess the appropriateness of registration accuracy tolerances proposed by the American Association of Physicists in Medicine Task Group 132 under such challenging conditions via assessment by expert observers.Methods and MaterialsFour-dimensional computed tomography scans for 12 patients with lung cancer were acquired with patients in prone and supine positions. Tumor and organs at risk were delineated by a physician on all data sets: supine inhale (SI), supine exhale, prone inhale, and prone exhale. The SI image was registered to the other images using both registration methods. All SI contours were propagated using the resulting transformations and compared with physician delineations using Dice similarity coefficient, mean distance to agreement, and Hausdorff distance. Additionally, propagated contours were anonymized along with ground-truth contours and rated for quality by physician-observers.ResultsAveraged across all patients, the accuracy metrics investigated remained within tolerances recommended by Task Group 132 (Dice similarity coefficient >0.8, mean distance to agreement <3 mm). MIM performed better with both complex (vertebrae) and low-contrast (esophagus) structures, whereas the in-house method performed better with lungs (whole and individual lobes). Accuracy metrics worsened but remained within tolerances when propagating from supine to prone; however, the Jacobian determinant contained regions with negative values, indicating localized nonphysiologic deformations. For MIM and in-house registrations, 50% and 43.8%, respectively, of propagated contours were rated acceptable as is and 8.2% and 11.0% as clinically unacceptable.ConclusionsThe deformable image registration methods performed reliably and met recommended tolerances despite anatomically challenging cases exceeding typical interfraction variability. However, additional quality assurance measures are necessary for complex applications (eg, dose propagation). Human review rather than unsupervised implementation should always be part of the clinical registration workflow.

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Cellular automata segmentation of the boundary between the compacta of vertebral bodies and surrounding structures

Cited by 2 publications

References 37 publications

Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application

Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application

Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations

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