Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT

Burns, Joseph E.; Yao, Jianhua; Muñoz, Hector E.; Summers, Ronald M.

doi:10.1148/radiol.2015142346

Cited by 64 publications

(52 citation statements)

References 37 publications

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“…Spine segmentation is a fundamental step for most subsequent spine image analysis and modeling tasks, such as identification of spine abnormalities (e.g. vertebral fractures, [1]), image-based biomechanical modeling (e.g. load analysis [2]) or image-guided spine intervention (vertebral fusion, [3]).…”

Section: Introductionmentioning

confidence: 99%

A multi-center milestone study of clinical vertebral CT segmentation

Yao

Burns

Forsberg

et al. 2016

Computerized Medical Imaging and Graphics

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A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.

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Section: Introductionmentioning

confidence: 99%

A multi-center milestone study of clinical vertebral CT segmentation

Yao

Burns

Forsberg

et al. 2016

Computerized Medical Imaging and Graphics

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120

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“…(15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection. (15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection.…”

Section: Radiographymentioning

confidence: 99%

“…One such system automatically detected acute traumatic fractures of thoracic and lumbar vertebral bodies on CT (24,53,54) (Fig. One such system automatically detected acute traumatic fractures of thoracic and lumbar vertebral bodies on CT (24,53,54) (Fig.…”

Section: Fracture Detectionmentioning

confidence: 99%

“…Examples of this work include automated detection of traumatic and compression fractures, degenerative changes, epidural masses, bone metastases, and bone mineral density of the spine on CT utilizing both second-and third-generation techniques. (15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection. (39)(40)(41)(42) Magnetic resonance imaging Because MRI is widely used for assessing spinal degenerative changes in the setting of neck and back pain, significant efforts have been made toward automated delineation of spinal anatomy on MRI images.…”

Section: Computed Tomographymentioning

confidence: 99%

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Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

Burns

Yao

Summers

2019

Journal of Bone and Mineral Research

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Artificial intelligence is upending many of our assumptions about the ability of computers to detect and diagnose diseases on medical images. Deep learning, a recent innovation in artificial intelligence, has shown the ability to interpret medical images with sensitivities and specificities at or near that of skilled clinicians for some applications. In this review, we summarize the history of artificial intelligence, present some recent research advances, and speculate about the potential revolutionary clinical impact of the latest computer techniques for bone and muscle imaging. © 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

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“…Traumatic vertebral body fracture detection software has been reported recently [139]. The software digitally strips the vertebral body cortex and looks for fractures in the cortical shell.…”

Section: Bonementioning

confidence: 99%

Progress in Fully Automated Abdominal CT Interpretation

Summers

2016

American Journal of Roentgenology

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OBJECTIVE Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically. CONCLUSION This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.

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Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT

Abstract: Purpose:To design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT).

Cited by 64 publications

References 37 publications

A multi-center milestone study of clinical vertebral CT segmentation

A multi-center milestone study of clinical vertebral CT segmentation

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

Progress in Fully Automated Abdominal CT Interpretation

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