Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping

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

doi:10.1007/978-3-642-33454-2_63

Cited by 65 publications

(53 citation statements)

References 9 publications

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“…Previously, we devised a decision support system for fracture detection on CT images (25). In that initial step, the system was designed to detect fracture lines on the vertebral body cortex.…”

Section: Discussionmentioning

confidence: 99%

“…Fracture detection in this preliminary work is limited to the vertebral body to simplify the topological analysis and to focus on structurally important Denis anterior and middle column injuries. A software algorithm was designed for fracture line detection on the vertebral body cortex (25 Figure 3. The testing and training sets did not demonstrate a statistically significant difference in sensitivity, with a bivariate x 2 test statistic of 0.074 (P = .79).…”

Section: Lesion Identification Digital Imaging and Communications Inmentioning

confidence: 99%

See 1 more Smart Citation

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

Burns¹,

Yao²,

Muñoz³

et al. 2016

Radiology

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Section: Discussionmentioning

confidence: 99%

Section: Lesion Identification Digital Imaging and Communications Inmentioning

confidence: 99%

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

Burns¹,

Yao²,

Muñoz³

et al. 2016

Radiology

Self Cite

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“…The analysis, quantification, and categorization of images with these methods is an important technique, which can improve patient safety and care. CAD systems have achieved breakthroughs in the detection of lesions [124,125], epidural masses [126], fractures [127], as well as a degenerative disease [128] and cancer [129]. Fisher's linear discriminant, Bayesian methods, artificial neural networks, and SVM are widely used as classifiers in CAD applications [130,13].…”

Section: Computer Aided Diagnosismentioning

confidence: 99%

Radiological images and machine learning: Trends, perspectives, and prospects

Zhang

Sejdić

2019

Computers in Biology and Medicine

156

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The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computeraided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.

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“…The main properties of data set are presented in Table I. Group [15]. The latter studies can be downloaded from a collaborative platform for research on spine imaging [16].…”

Section: A Materialsmentioning

confidence: 99%

A fully automated method for spinal canal detection in computed tomography images

Díaz-Parra

Arana

Moratal

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This work presents a new automated method for spinal canal detection in Computed Tomography (CT) images. It uses both 2D and 3D information and the algorithm extracts the spinal canal automatically. The procedure can be divided into three main steps. Firstly, a thresholding and a set of morphological operations were applied. Secondly, 3D connectivity analysis was defined to extract the objects forming part of the spinal canal. Finally, the centroid of each slice constituting the spinal canal object was computed. Furthermore, interpolation and extrapolation of data were performed, if required. The method was applied on two different groups, each one coming from different acquisition systems. A total of 25 patients and 8704 images were used. An experienced radiologist evaluated the method qualitatively supporting the utility of it, as all extracted points fell into the spinal canal. Therefore, our method was able to reduce the workload and detect spinal canal objectively. We expect to carry out a quantitative evaluation in our future research. The qualitative outcome of this work suggests promising results.

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Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping

Cited by 65 publications

References 9 publications

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

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

Radiological images and machine learning: Trends, perspectives, and prospects

A fully automated method for spinal canal detection in computed tomography images

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