Basion–Cartilaginous Dens Interval: An Imaging Parameter for Craniovertebral Junction Assessment in Children

Singh, Achint K.; Fulton, Zachary; Tiwari, Ruchi; Zhang, X.; Lu, Lijun; Altmeyer, Wilson; Tantiwongkosi, Bundhit

doi:10.3174/ajnr.a5400

Cited by 9 publications

(2 citation statements)

References 26 publications

(20 reference statements)

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“…However, the inconsistent boundary selection of the three cranial bones: the hard palate, opisthion, and the basion, are usually isolated to the posterior ends of each bone structure, areas that are not considered for calculating TAOD related metrics. For example, the TAOD metrics such as the BDI, which takes the distance between the inferior (lowest) point of the basion and the closest point of superior C2 9 as shown in Fig. 1 b.…”

Section: Discussionmentioning

confidence: 99%

“…Images of the cervical spine are taken either through MRI, CT, or X-rays, then studied for particular dislocations of specific cervical and cranial areas. One metric used to diagnose TAOD is the 10 mm increase in the basion-dens interval (BDI), which refers to the distance between the basion and the top of the C1 cervical bone by 10 mm 9 . Another metric is the Power’s ratio, which refers to the ratio of the distance between the opisthion to the anterior arch of C1, and the distance between the basion to the posterior arch of C1 10 .…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of U-Net models in automated cervical spine and cranial bone segmentation using X-ray images for traumatic atlanto-occipital dislocation diagnosis

Shim

Kim

et al. 2022

Sci Rep

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Segmentation of the cervical spine in tandem with three cranial bones, hard palate, basion, and opisthion using X-ray images is crucial for measuring metrics used to diagnose traumatic atlanto-occipital dislocation (TAOD). Previous studies utilizing automated segmentation methods have been limited to segmenting parts of the cervical spine (C3 ~ C7), due to difficulties in defining the boundaries of C1 and C2 bones. Additionally, there has yet to be a study that includes cranial bone segmentations necessary for determining TAOD diagnosing metrics, which are usually defined by measuring the distance between certain cervical (C1 ~ C7) and cranial (hard palate, basion, opisthion) bones. For this study, we trained a U-Net model on 513 sagittal X-ray images with segmentations of both cervical and cranial bones for an automated solution to segmenting important features for diagnosing TAOD. Additionally, we tested U-Net derivatives, recurrent residual U-Net, attention U-Net, and attention recurrent residual U-Net to observe any notable differences in segmentation behavior. The accuracy of U-Net models ranged from 99.07 to 99.12%, and dice coefficient values ranged from 88.55 to 89.41%. Results showed that all 4 tested U-Net models were capable of segmenting bones used in measuring TAOD metrics with high accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%