Context sensitive labeling of spinal structure in MR images

Bhole, Chetan; Kompalli, Suryaprakash; Chaudhary, Vipin

doi:10.1117/12.812191

Cited by 22 publications

(6 citation statements)

References 18 publications

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“…A number of studies have explored the proper ways to AIassisted automate label the disc/vertebra/spinal cord. 14,15 For example, the geometric information and image features of disc herniation were able to be quantitively captured and labeled then for disease diagnose. As these AI-reaching accuracy in identification not reaching 100%, we prefer a manual easy picking of the vertebra and spinal cord to completely collect MRI data.…”

Section: Vertebra/spinal Cord Labeling Modulementioning

confidence: 99%

A New Automated AI-Assisted System to Assess Cervical Disc Herniation

Zhang

et al. 2022

Spine

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Study Design. An algorithm was developed with MATLAB platform to automatically quantify the volume of cervical disc herniation (CDH) based on the sagittal magnetic resonance images. This automated program was used for CDH data set, and then compared with manual measurement results confirming its reliability. Objective. The aim was to develop a new software for automated CDH volume measurement. Summary of Background Data. CDH compresses the spinal cord, regarding as the leading cause of cervical myelopathy. However, the CDH volume, of great value to clinical symptoms, can be only manually measured with no-excellent but acceptable interobserver reliability. This was due to the manual error of outlining CDH area and inclusion of structure posterior vertebra. No studies has proposed such an automated algorithm of CDH volume quantification which is standardised to quantify the accurate volume of CDH thus helping doctors easily evaluate CDH progressing. Methods. The algorithm of CDH volume measurement was proposed. This program was then tested for 490 CDHs data set, from 185 patients with two repeated magnetic resonance imaging detections. Three individual observers manually measured the volumes of these CDHs, to justify the accuracy of this software. CDH volume was either in the classic way or the revised way excluding the influence of structure posterior vertebra. Results. The automated software was successfully developed on MATLAB platform, with no difference found with manual measurements (average level) in CDH volume measurement. The change ratios in CDH volumes were profoundly consistent with manual observation, showing the error of 5.8% in median. The revised method elevated the absolute value of ratio by amplifying the percentage change. Conclusion. Our developed automated volumetry system was an standardized and accurate way, with selective removal module of structure posterior vertebra, replaceable for manual volume measurement of CDH, which was useful for spinal surgeons diagnosing and treating CDH disease.

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Section: Vertebra/spinal Cord Labeling Modulementioning

confidence: 99%

A New Automated AI-Assisted System to Assess Cervical Disc Herniation

Zhang

et al. 2022

Spine

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“…− Computation of intensity difference between a T1-weighted sagittal slice and a T2-weighted sagittal slice. The difference between the T2-weighted and the corresponding T1-weighted is known to highlight the regions that are high in water content and low in fat content [12]. Thus, we employ this region for subsequent processing.…”

Section: A Lumbardiagnositcs Frameworkmentioning

confidence: 99%

“…1(b) [12]. Thus, we use this difference image for tracing spinal canal boundaries in two different ways: one based on intensity values of the image, and the other based on the gradient of the image.…”

Section: Introductionmentioning

confidence: 99%

Automated boundary extraction of the spinal canal in mri based on dynamic programming

Koh

Chaudhary

Dhillon

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The spinal cord is the only communication link between the brain and the body. The abnormalities in it can lead to severe pain and sometimes to paralysis. Due to the growing gap between the number of available radiologists and the number of required radiologists, the need for computer-aided diagnosis and characterization is increasing. To ease this gap, we have developed a computer-aided diagnosis and characterization framework in lumbar spine that includes the spinal cord, vertebrae, and intervertebral discs. In this paper, we propose two spinal cord boundary extraction methods that fit into our framework based on dynamic programming in lumbar spine MRI. Our method incorporates the intensity of the image and the gradient of the image into a dynamic programming scheme and works in a fully-automatic fashion. The boundaries generated by our method is compared against reference boundaries in terms of Fréchet distance which is known to be a metric for shape analysis. The experimental results from 65 clinical data show that our method finds the spinal canal boundary correctly achieving a mean Fréchet distance of 13.5 pixels. For almost all data, the extracted boundary falls within the spinal cord. So, it can be used as a landmark when marking background regions and finding regions of interest.

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“…where the functions U and R capture the positional and orientational differences between neighboring disc variables x k and x k+1 , respectively, and D(x k , α) captures the Euclidean distance to the spinal cord whose position is calculated using the method of [2]. Let y i = {y 1 , y 2 , .…”

Section: Disc Center Localization With the Graphical Modelmentioning

confidence: 99%

Localization of the Lumbar Discs Using Machine Learning and Exact Probabilistic Inference

Oktay

Akgül

2011

Lecture Notes in Computer Science

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Abstract. We propose a novel fully automatic approach to localize the lumbar intervertebral discs in MR images with PHOG based SVM and a probabilistic graphical model. At the local level, our method assigns a score to each pixel in target image that indicates whether it is a disc center or not. At the global level, we define a chain-like graphical model that represents the lumbar intervertebral discs and we use an exact inference algorithm to localize the discs. Our main contributions are the employment of the SVM with the PHOG based descriptor which is robust against variations of the discs and a graphical model that reflects the linear nature of the vertebral column. Our inference algorithm runs in polynomial time and produces globally optimal results. The developed system is validated on a real spine MRI dataset and the final localization results are favorable compared to the results reported in the literature.

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Context sensitive labeling of spinal structure in MR images

Cited by 22 publications

References 18 publications

A New Automated AI-Assisted System to Assess Cervical Disc Herniation

A New Automated AI-Assisted System to Assess Cervical Disc Herniation

Automated boundary extraction of the spinal canal in mri based on dynamic programming

Localization of the Lumbar Discs Using Machine Learning and Exact Probabilistic Inference

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