Automated landmarking and labeling of fully and partially scanned spinal columns in CT images

Major, David C.; Hladůvka, Jiřı́; Schulze, Florian; Bühler, Katja

doi:10.1016/j.media.2013.07.005

Cited by 25 publications

(13 citation statements)

References 11 publications

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“…Another option would be that the expert segmented the spinal canal to evaluate the algorithm in terms of true positives (TP) and false positives (FP). Thus, detected points within segmented spinal canal would be TP, and FP otherwise, similar to [11]. In any way, the obtained outcome in this work suggests promising results.…”

Section: Discussionmentioning

confidence: 55%

“…According to Major et al [11], spine detection methods can be divided into two categories: data-driven and anatomical model-driven methods. The first approach uses minimal or no anatomical knowledge without machine learning whereas the latter learns characteristics of a training dataset as well as integrating a considerable amount of anatomical knowledge.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 55%

Section: Introductionmentioning

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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“…Adding the 4 cases of correction of failed labeling, our application achieved a correct labeling in 99%. Klinder [27][28][29]. Due to a user-friendly revision tool, manual correction of erroneous labeling was achieved in 22.6 s on average.…”

Section: Discussionmentioning

confidence: 99%

First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT

Scholtz

Wichmann

Kaup

et al. 2015

European Journal of Radiology

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“…First, they relaxed input data requirements found in the literature and labelled both full and partial spine scans. Secondly, they intended to fulfill the performance requirement for daily clinical use and developed a high throughput system capable of processing thousands of slices in just a few minutes [13].…”

Section: Related Workmentioning

confidence: 99%

A Novel Method VESTAL to Label Lumber Vertebrae and Intervertebral Discs

Srinivas¹,

Ramana²

2015

IJCA

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This paper presents a novel method Vertebrae Statistics description Algorithm (VESTAL) to label lumber vertebrae and intervertebral discs (IVDs). Each vertebra and IVD has certain statistical features and properties. To label vertebrae and IVDs, a new equation to model the path of spinal cord is derived using statistical properties of the spinal canal. VESTAL uses this equation for labelling Lumber vertebrae and IVDs by determining both posterior, interior width and heights. The calculated values are compared with real values which are measured using scale and the comparison produced 95 % efficiency and accuracy in results. The VESTAL is applied on 50 patients, 250 MR images and obtained 96% accuracy in labelling.

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Automated landmarking and labeling of fully and partially scanned spinal columns in CT images

Cited by 25 publications

References 11 publications

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

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

First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT

A Novel Method VESTAL to Label Lumber Vertebrae and Intervertebral Discs

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