A high-quality dataset featuring classified and annotated cervical spine X-ray atlasIMAGE recognition (Computer vision)SCIENTIFIC communityCOMPUTER-assisted image analysis (Medicine)SPINE diseasesMACHINE learningRecent research in computational imaging largely focuses on developing machine learning (ML) ...
. On the other hand, different methods have been employed, such as electrogoniometer-based direct measurement (Slavia et al., 2006) and X-ray- (Anderst et al., 2011) or fluoroscopy- (Lin et al., 2014) based imaging track systems. In comparison, an image-based method is more robust ...
Lateral X-ray images of the cervical spine in patients with cervical ossification of the posterior longitudinal ligament (C-OPLL). The white line represents the K-line, which is drawn by connecting the midpoints of the anterior-posterior diameter of the spinal canal at C2 and C7. (A) The...
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A Random Forest classifier labels regions as degenerative change or normal. Leave-one-out cross-validation studies performed on a dataset of 103 patients demonstrates performance of above 95% accuracy. 展开 关键词: Spine X-rays Machine learning Neck ...
This study has provided a normative dataset for selected measurement parameters derived from specific cervical vertebral segments in an asymptomatic population of young adults. Across the total cohort there was considerable variation in each of the selected measurement parameters. There was a significant ...
The superiority of MIP over conventional X-ray measurements of the intervertebral space lies in its ability to more accurately display the boundaries of the intervertebral space and selectively exclude the influence of other levels on area measurements. In our study, no significant differences were ...
The correlation between radiological and clinical findings to distinguish between symptomatic and asymptomatic patients is, however, limited and unreliable for all common modalities such as X-ray, computed tomography, MRI scan or single-photon emission computed tomography (SPECT) scan [32,33,34,35]....
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This work aims at developing and evaluating a deep learning-based framework, named VinDr-SpineXR, for the classification and localization of abnormalities from spine X-rays. First, we build a large dataset, comprising 10,468 spine X-ray images from 5,000 studies, each of which is manually ...