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Cervical spine abnormal x ray3/30/2024 To tackle this challenge, we focus on creating a significant benchmark dataset of spine X-rays that are manually annotated at the lesion level by experienced radiologists. The lack of large datasets with high-quality images and human experts' annotations is the key obstacle. To the best of our knowledge, no existing studies are devoted to developing and evaluating a comprehensive system for classifying and localizing multiple spine lesions from X-ray scans. Most of these studies focus on automated fracture detection and localization. ![]() These factors could lead to the risk of missing significant findings, resulting in severe consequences for patients and clinicians.Ĭurrently, deep convolutional networks (CNNs) have shown significant improvements in the musculoskeletal analysis from X-rays. The interpretation of spine X-ray images requires an in-depth understanding of diagnostic radiography, in which large variability in the number, size, and general appearance of spine lesions makes this a complex and time-consuming task. It has been the primary tool widely used to identify and monitor various spine abnormalities such as fractures, osteophytes, thinning of the bones, vertebral collapse, or tumors. The most simple and accessible modality, conventional radiograph, still plays an essential role in studying spinal disorders despite the rapid development of advanced imaging techniques such as MRI and CT. Spinal-related conditions account for the central portion of the overall burden of musculoskeletal conditions. This is the largest dataset to date that provides radiologist's bounding-box annotations for developing supervised-learning algorithms for spine X-ray analysis. The dataset, called VinDr-SpineXR, contains 10,466 spine X-ray images from 5,000 studies, each of which is manually annotated with 13 types of abnormalities by an experienced radiologist with bounding boxes around abnormal findings. To fill this gap, we introduce a large-scale annotated medical image dataset for spinal lesion detection and classification from radiographs. The lack of large-scale spine X-ray datasets with high-quality images and human expert annotations is the key obstacle. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. Radiographs are used as the most critical imaging tool for identifying spine anomalies in clinical practice. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov PC, Mark R, Mietus JE, Moody GB, Peng CK, Stanley HE. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Mietus, J.E., Moody, G.B., Peng, C.K. ![]() "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs (version 1.0.0). (2021) 'VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs' (version 1.0.0), PhysioNet. "VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs" (version 1.0.0). ![]() Pham, Hieu Huy, Nguyen Trung, Hieu, and Ha Quy Nguyen.
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