A Novel Automatic Segmentation Method to Quantify the Effects of Spinal Cord Injury on Human Thigh Muscles and Adipose Tissue

Mesbah, Samineh; Shalaby, Ahmed; Stills, Sean; Soliman, Ahmed; Willhite, Andrea; Harkema, Susan J.; Rejc, Enrico; El-Baz, Ayman

doi:10.1007/978-3-319-66185-8_79

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…12 Therefore, there is great need for (semi-) automatic segmentation tools in the context of quantitative imaging of skeletal muscle. Recently, some (semi-) automatic approaches have been proposed for skeletal muscle [12][13][14][15][16][17][18][19][20] and showed good correspondence, reflected by high Dice similarity coefficient (DSC) values, with manual segmentation. However, these approaches only focused on either a partial volume of the muscles or entire muscle groups rather than the full volume of individual muscles.…”

Section: Introductionmentioning

confidence: 99%

Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle

et al. 2020

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Diffusion tensor imaging (DTI) is becoming a relevant diagnostic tool to understand muscle disease and map muscle recovery processes following physical activity or after injury. Segmenting all the individual leg muscles, necessary for quantification, is still a time-consuming manual process. The purpose of this study was to evaluate the impact of a supervised semi-automatic segmentation pipeline on the quantification of DTI indices in individual upper leg muscles. Longitudinally acquired MRI datasets (baseline, post-marathon and follow-up) of the upper legs of 11 subjects were used in this study. MR datasets consisted of a DTI and Dixon acquisition. Semi-automatic segmentations for the upper leg muscles were performed using a transversal propagation approach developed by Ogier et al on the out-of-phase Dixon images at baseline. These segmentations were longitudinally propagated for the post-marathon and follow-up time points. Manual segmentations were performed on the water image of the Dixon for each of the time points. Dice similarity coefficients (DSCs) were calculated to compare the manual and semi-automatic segmentations. Bland-Altman and regression analyses were performed, to evaluate the impact of the two segmentation methods on mean diffusivity (MD), fractional anisotropy (FA) and the third eigenvalue (λ 3). The average DSC for all analyzed muscles over all time points was 0.92 ± 0.01, ranging between 0.48 and 0.99. Bland-Altman analysis showed that the 95% limits of agreement for MD, FA and λ 3 ranged between 0.5% and 3.0% for the transversal propagation and between 0.7% and 3.0% for the longitudinal propagations. Similarly, regression analysis showed good correlation for MD, FA and λ 3 (r = 0.99, p < 60; 0.0001). In conclusion, the supervised semi-automatic segmentation framework successfully quantified DTI indices in the upper-leg muscles compared with manual segmentation while only requiring manual input of 30% of the slices, resulting in a threefold reduction in segmentation time.

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Section: Introductionmentioning

confidence: 99%

Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle

et al. 2020

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“…This means that the receptive field of a CRF is the entire image. Graphical models such as the fully-connected CRF have also been extensevely used in 3D medical imaging segmentation with good resuts [2,4,5,10,12]. One of the issues with applying a fully-connected CRF to 3D images is the fact that the third dimension introduces exponentially more pairs of hyper-voxels in the graph, also known as cliques.…”

Section: Introductionmentioning

confidence: 99%

Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation

Monteiro,

Figueiredo,

Oliveira

2018

Preprint

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The Conditional Random Field as a Recurrent Neural Network layer is a recently proposed algorithm meant to be placed on top of an existing Fully-Convolutional Neural Network to improve the quality of semantic segmentation. In this paper, we test whether this algorithm, which was shown to improve semantic segmentation for 2D RGB images, is able to improve segmentation quality for 3D multi-modal medical images. We developed an implementation of the algorithm which works for any number of spatial dimensions, input/output image channels, and reference image channels. As far as we know this is the first publicly available implementation of this sort. We tested the algorithm with two distinct 3D medical imaging datasets, we concluded that the performance differences observed were not statistically significant. Finally, in the discussion section of the paper, we go into the reasons as to why this technique transfers poorly from natural images to medical images.

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“…The ability to stand with independent lower limb extension is a key achievement toward the recovery of functional mobility, and it was consistently observed in all three motor complete SCI individuals previously reported and subsequently they recovered over ground stepping and walking [8,12]. The guidelines proposed for selecting a sub-set of electrode configurations to be tested for standing include adjusting cathodes (active electrodes) positon in order to target primarily extensors muscle groups according to the individualized map of motor pools activation [116,118]. Also, the use of multiple interleaving programs represents an important advantage compared to the use of a single program, as it allows to access different locations of the spinal circuitry with different intensities.…”

Section: A Introductionmentioning

confidence: 93%

Automatic signal and image-based assessments of spinal cord injury and treatments.

Mesbah¹

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A Novel Automatic Segmentation Method to Quantify the Effects of Spinal Cord Injury on Human Thigh Muscles and Adipose Tissue

Cited by 4 publications

References 15 publications

Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle

Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle

Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation

Automatic signal and image-based assessments of spinal cord injury and treatments.

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