A novel segmentation framework dedicated to the follow‐up of fat infiltration in individual muscles of patients with neuromuscular disorders

Ogier, Augustin C.; Heskamp, Linda; Michel, Constance; Fouré, Alexandre; Bellemare, Marc‐Emmanuel; Troter, Arnaud Le; Heerschap, Arend; Bendahan, David

doi:10.1002/mrm.28030

Cited by 23 publications

(45 citation statements)

References 32 publications

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“…Our method shows advantages compared to other studies that tackled this challenging task in recent years [21][22][23][24][25]. Kemnitz et al [21] developed a semiautomated thigh muscle segmentation method using an active shape model.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the above studies did not perform reproducibility analysis. Ogier et al [25] designed a semiautomatic segmentation pipeline of individual leg muscles using two-point Dixon and three-point Dixon images based on automatic propagation through nonlinear registrations of initial delineation. The reproducibility was also assessed on four subjects scanned twice on the same day.…”

Section: Discussionmentioning

confidence: 99%

“…Such a process can lower the accuracy and reproducibility, which is also not feasible for clinical adoption. Although recent studies developed semiautomated or automated algorithms [21][22][23][24][25], they are either based on nonquantitative MRI or difficult to apply in clinical settings [26]. A faster, simpler and fully automated thigh muscle segmentation method for reproducible fat fraction quantification is highly desired.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat–water decomposition MRI

et al. 2020

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Background Time-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat–water decomposition MRI. Results This study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs. Conclusions This automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat–water decomposition MRI

et al. 2020

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“…The purpose of the present study was to evaluate a supervised muscle segmentation framework developed by Ogier et al, 21,22 for the quantification of DTI indices in individual muscles of the thighs. Using this tool, we assessed the accuracy, feasibility and impact on the quantification of DTI indices in comparison with manual segmentation as well as the reduction in work load compared with manual segmentation.…”

Section: Discussionmentioning

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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“…The five segmented masks were propagated to the remaining slices of the volume of interest (2D+t) using an automatic label propagation algorithm, as previously described 17 (Figure 2B). This algorithm, implemented with the ANTs library, 18 consisted of a registration process with rigid and affine transformations, followed by a diffeomorphic multilevel registration with B‐spline regularization.…”

Section: Methodsmentioning

confidence: 99%

Semiautomatic quantification of abdominal wall muscles deformations based on dynamic MRI image registration

et al. 2021

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Quantitative analysis of abdominal organs motion and deformation is crucial to better understand biomechanical alterations undermining respiratory, digestive or perineal pathophysiology. In particular, biomechanical characterization of the antero‐lateral abdominal wall is central in the diagnosis of abdominal muscle deficiency. Here, we present a dedicated semiautomatic dynamic MRI postprocessing method enabling the quantification of spatial and temporal deformations of the antero‐lateral abdominal wall muscles. Ten healthy participants were imaged during a controlled breathing session at the L3–L4 disc level using real‐time dynamic MRI at 3 T. A coarse feature‐tracking step allowed the selection of the inhalation cycle of maximum abdominal excursion. Over this image series, the described method combines (1) a supervised 2D+t segmentation procedure of the abdominal wall muscles, (2) the quantification of muscle deformations based on masks registration, and (3) the mapping of deformations within muscle subzones leveraging a dedicated automatic parcellation. The supervised 2D+t segmentation (1) provided an accurate segmentation of the abdominal wall muscles throughout maximum inhalation with a 0.95 ± 0.03 Dice similarity coefficient (DSC) value and a 2.3 ± 0.7 mm Hausdorff distance value while requiring only manual segmentation of 20% of the data. The robustness of the deformation quantification (2) was indicated by high indices of correspondence between the registered source mask and the target mask (0.98 ± 0.01 DSC value and 2.1 ± 1.5 mm Hausdorff distance value). Parcellation (3) enabled the distinction of muscle substructures that are anatomically relevant but could not be distinguished based on image contrast. The present genuine postprocessing method provides a quantitative analytical frame that could be used in further studies for a better understanding of abdominal wall deformations in physiological and pathological situations.

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A novel segmentation framework dedicated to the follow‐up of fat infiltration in individual muscles of patients with neuromuscular disorders

Cited by 23 publications

References 32 publications

Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat–water decomposition MRI

Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat–water decomposition MRI

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

Semiautomatic quantification of abdominal wall muscles deformations based on dynamic MRI image registration

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