MRI-Based 3D Shape Analysis of Thigh Muscles

HajGhanbari, Bahareh; Hamarneh, Ghassan; Changizi, Neda; Ward, Aaron D.; Reid, W. Darlene

doi:10.1016/j.acra.2010.09.008

Cited by 19 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Methods to estimate skeletal muscle volumes and/or 3D muscle shapes using MRI data are used clinically for diagnosis [14], to evaluate the effects of treatment [12], and as an aid to preoperative planning [18]. In the case of muscle pathologies, changes in muscle shape and signal occur because of muscle degeneration, which can render identification of muscle boundaries in MRI difficult (due to fatty and fibrous infiltration) [16,17]. Modification of the anatomical landmarks used for CSA segmentation, of techniques that are based on shape factors, and of volume estimation methods may therefore be required.…”

Section: Discussionmentioning

confidence: 99%

“…Measurement of muscle volume facilitates surveillance of neuromuscular disease progression [10,11] and the effects of treatments [12,13], as well as being useful for diagnostic purposes [14,15]. Muscle shapes can be used to distinguish between pathologies [16,17] and modelling individual muscles can be useful when planning surgery [18], evaluating changes over time [6,19] and in order to improve the understanding of particular symptoms or diseases [16,17,20–22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability

et al. 2018

View full text Add to dashboard Cite

AimsThe aim of this study was to report the metrological qualities of techniques currently used to quantify skeletal muscle volume and 3D shape in healthy and pathological muscles.MethodsA systematic review was conducted (Prospero CRD42018082708). PubMed, Web of Science, Cochrane and Scopus databases were searched using relevant keywords and inclusion/exclusion criteria. The quality of the articles was evaluated using a customized scale.ResultsThirty articles were included, 6 of which included pathological muscles. Most evaluated lower limb muscles. Partially or completely automatic and manual techniques were assessed in 10 and 24 articles, respectively. Manual slice-by-slice segmentation reliability was good-to-excellent (n = 8 articles) and validity against dissection was moderate to good(n = 1). Manual slice-by-slice segmentation was used as a gold-standard method in the other articles. Reduction of the number of manually segmented slices (n = 6) provided good to excellent validity if a sufficient number of appropriate slices was chosen. Segmentation on one slice (n = 11) increased volume errors. The Deformation of a Parametric Specific Object (DPSO) method (n = 5) decreased the number of manually-segmented slices required for any chosen level of error. Other automatic techniques combined with different statistical shape or atlas/images-based methods (n = 4) had good validity. Some particularities were highlighted for specific muscles. Except for manual slice by slice segmentation, reliability has rarely been reported.ConclusionsThe results of this systematic review help the choice of appropriate segmentation techniques, according to the purpose of the measurement. In healthy populations, techniques that greatly simplified the process of manual segmentation yielded greater errors in volume and shape estimations. Reduction of the number of manually segmented slices was possible with appropriately chosen segmented slices or with DPSO. Other automatic techniques showed promise, but data were insufficient for their validation. More data on the metrological quality of techniques used in the cases of muscle pathology are required.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability

et al. 2018

View full text Add to dashboard Cite

show abstract

“…As mentioned previously, the cylindrical model heavily relies on the assumption of evenly distributed muscle mass throughout the limb ( 27 ). However, it has been shown that in clinical cohorts, the quadriceps display significant irregularities along the muscle length when compared with healthy individuals ( 28 ). Furthermore, it is likely that loss of muscle mass does not occur uniformly ( 29 , 30 ), which could further compound the irregularity of muscle distribution and hence impact MV estimations via the cylindrical equation ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, it has been shown that in clinical cohorts, the quadriceps display significant irregularities along the muscle length when compared with healthy individuals ( 28 ). Furthermore, it is likely that loss of muscle mass does not occur uniformly ( 29 , 30 ), which could further compound the irregularity of muscle distribution and hence impact MV estimations via the cylindrical equation ( 28 ). In addition to changes in the quantity of muscle, there are also likely adverse changes to muscle quality, such as the infiltration of fat.…”

Section: Discussionmentioning

confidence: 99%

The Impact of Slice Interval and Equation on the Accuracy of Magnetic Resonance Image Estimation of Quadriceps Muscle Volume in End Stage Liver Disease

Quinlan

Jones

Bissonnette

et al. 2022

Front. Rehabilit. Sci.

View full text Add to dashboard Cite

IntroductionEnd stage liver disease (ESLD) is associated with loss of muscle mass and function, known as sarcopenia, which can increase the risk of complications of ESLD, hospitalization and mortality. Therefore, the accurate assessment of muscle mass is essential to evaluate sarcopenia in ESLD. However, manual segmentation of muscle volume (MV) can be laborious on cross-sectional imaging, due to the number of slices that require analysis. This study aimed to investigate the impact of reducing the number of slices required for MV estimation. Further, we aimed to compare two equations utilized in estimating MV (cylindrical and truncated cone).MethodsThirty eight ESLD patients (23 males; 54.8 ± 10.7 years) were recruited from the Queen Elizabeth University Hospital Birmingham. A 3T MRI scan was completed of the lower limbs. Quadriceps MV was estimated utilizing 1-, 2-, 3-, and 4 cm slice intervals with both cylindrical and truncated cone equations. Absolute and relative error (compared to 1 cm slice interval) was generated for 2-, 3-, and 4 cm slice intervals. L3 skeletal muscle index (SMI) was also calculated in 30 patients.ResultsRelative error increased with slice interval using the cylindrical (0.45 vs. 1.06 vs. 1.72%) and truncated cone equation (0.27 vs. 0.58 vs. 0.74%) for 2, 3, and 4 cm, respectively. Significantly, the cylindrical equation produced approximately twice the error compared to truncated cone, with 3 cm (0.58 vs. 1.06%, P < 0.01) and 4 cm intervals (0.74 vs. 1.72%, P < 0.001). Finally, quadriceps MV was significantly correlated to L3 SMI (r2 = 0.44, P < 0.0001).ConclusionThe use of the truncated equation with a 4 cm slice interval on MRI offers an efficient but accurate estimation of quadricep muscle volume in ESLD patients.

show abstract

“…Recently, Active Shape Models (ASMs) (Cootes et al 1995) have proved successful in automatically segmenting 3D MR images for a number of anatomical structures in humans including the (individual) bones (Fripp et al 2010;Schmid, Kim and Magnenat-Thalmann 2011;Xia et al 2013;Chandra et al 2014), muscles (Engstrom et al 2011;HajGhanbari et al 2011), prostate (Martin, Troccaz and Daanen 2010;Chandra et al 2012), liver (Heimann, Wolf and Meinzer 2006) and the heart (Zheng et al 2008;Ecabert et al 2008). They have even been utilised in the shape analysis of structures such as the hippocampus of the brain in Alzheimer's disease (Shen et al 2012) and inter-vertebral discs with degenerative-type changes of the lumbar spine (Neubert et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Fast automated segmentation of multiple objects via spatially weighted shape learning

et al. 2016

View full text Add to dashboard Cite

Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice's similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12-15 min, nearly an order of magnitude faster than the multi-atlas approach.

show abstract

MRI-Based 3D Shape Analysis of Thigh Muscles

Cited by 19 publications

References 31 publications

Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability

Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability

The Impact of Slice Interval and Equation on the Accuracy of Magnetic Resonance Image Estimation of Quadriceps Muscle Volume in End Stage Liver Disease

Fast automated segmentation of multiple objects via spatially weighted shape learning

Contact Info

Product

Resources

About