Clinically desired segmentation method for vertebral bodies

Aslan, Melih S.; Shalaby, Ahmed; Farag, Aly A.

doi:10.1109/isbi.2013.6556606

Cited by 19 publications

(15 citation statements)

References 18 publications

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“…The results show that A1 − A3 need perfect manual initialisations; whereas our method is almost independent of the initialisation. Since VBs in the ESP data set do not have rotational difference, the proposed method obtained very close segmentation results as in [22].…”

Section: Fig 15 Effects Of Each Model On Segmentationmentioning

confidence: 68%

Vertebral body segmentation using a probabilistic and universal shape model

Aslan

Shalaby

Farag

2015

IET Computer Vision

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Osteoporosis is a bone disease characterised by a reduction in bone mass, resulting in an increased risk of fractures. Doctors need the bone mineral density (BMD) measurements of vertebral bodies in order to diagnose and treat osteoporosis. The authors' objective is to segment the VBs as accurately as possible and hence to increase the accuracy of the BMD measurements and fracture analysis. Three pieces of information (intensity, spatial interaction and shape) are modelled to optimise a probabilistic energy functional. A universal shape prior, which is modelled using the cervical, thoracic and lumbar spinal regions, is proposed. Volumetric computed tomography data sets with various challenges are used in this study. The authors classify data sets based on some features related to the anatomy, imaging modality and level of the bone health. The proposed framework is one of only a few reported in the literature tested on the data obtained from different imaging devices. The experimental results reveal that the proposed method is robust under various noise levels, less variant to the initialisation and faster than existing vertebrae segmentation reports in the literature.

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Section: Fig 15 Effects Of Each Model On Segmentationmentioning

confidence: 68%

Vertebral body segmentation using a probabilistic and universal shape model

Aslan

Shalaby

Farag

2015

IET Computer Vision

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“…Chen et al [21] have already demonstrated the feasibility of segmentation of lungs, liver, spleen and kidneys via DECT and the U-Net. The proposed algorithm outperformed the traditional JJ2016 algorithm [18] both in speed and accuracy; the computation time was shortened from 10-15 min to about 5 s. Other traditional segmentation algorithms may have notably shorter execution times, for instance Aslan et al [35] report 116 s for the segmentation of vertebrae in 100 slices. However, it is not clear how the algorithm would handle the segmentation of all bones in the pelvic region, where the main problem in our application is the hole filling.…”

Section: Discussionmentioning

confidence: 95%

Segmentation of bones in medical dual-energy computed tomography volumes using the 3D U-Net

et al. 2020

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Deep learning algorithms have improved the speed and quality of segmentation for certain tasks in medical imaging. The aim of this work is to design and evaluate an algorithm capable of segmenting bones in dual-energy CT data sets. A convolutional neural network based on the 3D U-Net architecture was implemented and evaluated using high tube voltage images, mixed images and dual-energy images from 30 patients. The network performed well on all the data sets; the mean Dice coefficient for the test data was larger than 0.963. Of special interest is that it performed better on dual-energy CT volumes compared to mixed images that mimicked images taken at 120 kV. The corresponding increase in the Dice coefficient from 0.965 to 0.966 was small since the enhancements were mainly at the edges of the bones. The method can easily be extended to the segmentation of multi-energy CT data.

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“…Several researchers have come up with discrepant methods to tackle this initialization problem. For instance, Aslan et al [ 5 , 17 , 18 ] have integrated intensity, spatial interaction, and shape information into a probabilistic energy model in order to obtain the optimum segmentation. Shalaby et al [ 19 , 20 ] have used a two-dimensional principal component analysis to extract the shape prior information in order to initialize level set function and constructed a probabilistic shape-based model.…”

Section: Related Workmentioning

confidence: 99%

Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation

Yang

Liang

Zhang

et al. 2018

BioMed Research International

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Vertebrae computed tomography (CT) image automatic segmentation is an essential step for Image-guided minimally invasive spine surgery. However, most of state-of-the-art methods still require human intervention due to the inherent limitations of vertebrae CT image, such as topological variation, irregular boundaries (double boundary, weak boundary), and image noise. Therefore, this paper intentionally designed an automatic global level set approach (AGLSA), which is capable of dealing with these issues for lumbar vertebrae CT image segmentation. Unlike the traditional level set methods, we firstly propose an automatically initialized level set function (AILSF) that comprises hybrid morphological filter (HMF) and Gaussian mixture model (GMM) to automatically generate a smooth initial contour which is precisely adjacent to the object boundary. Secondly, a regularized level set formulation is introduced to overcome the weak boundary leaking problem, which utilizes the region correlation of histograms inside and outside the level set contour as a global term. Ultimately, a gradient vector flow (GVF) based edge-stopping function is employed to guarantee a fast convergence rate of the level set evolution and to avoid level set function oversegmentation at the same time. Our proposed approach has been tested on 115 vertebrae CT volumes of various patients. Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise.

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Clinically desired segmentation method for vertebral bodies

Cited by 19 publications

References 18 publications

Vertebral body segmentation using a probabilistic and universal shape model

Vertebral body segmentation using a probabilistic and universal shape model

Segmentation of bones in medical dual-energy computed tomography volumes using the 3D U-Net

Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation

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