Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle

Popuri, Karteek; Cobzaş, Dana; Esfandiari, Nina; Baracos, Vickie E.; Jägersand, Martin

doi:10.1109/tmi.2015.2479252

Cited by 115 publications

(117 citation statements)

References 36 publications

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“…CT images were examined using Impax radiological software (AGFA-version 6, Morstel, Belgium) and transverse sections at the level of L3 were extracted for our analyses. L3 lumbar segments were processed using automated image segmentation software,(12, 13) The software recognizes muscle tissue based on density threshold between −29 and +150 HU, while using a priori information about the L3 muscle shape to avoid mislabeling parts of the neighboring organs that also have HU values in the −29 +150 range. The program provides a highly accurate, unbiased estimation of the cross-sectional lean tissue area and skeletal muscle area.…”

Section: Methodsmentioning

confidence: 99%

Skeletal Muscle Measures as Predictors of Toxicity, Hospitalization, and Survival in Patients with Metastatic Breast Cancer Receiving Taxane-Based Chemotherapy

Shachar

Deal

Weinberg

et al. 2017

Clinical Cancer Research

229

192

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Purpose Severe skeletal muscle (SM) loss (sarcopenia), is associated with poor cancer outcomes including reduced survival and increased toxicity. This study investigates SM measures in metastatic breast cancer (MBC) patients receiving first line taxane-based chemotherapy and evaluates associations with treatment toxicity and other outcomes. Experimental Design Using computerized tomography (CT) images taken for the evaluation of disease burden, skeletal muscle area (SMA) and density (SMD) were measured at the 3rd lumbar vertebrae. Sarcopenia was defined as Skeletal Muscle Index (SMI=SMA/height2) ≤41. Skeletal Muscle Gauge (SMG) was created by multiplying SMI x SMD. Fisher’s exact tests, t-tests, the Kaplan-Meier method, and Cox regression modeling were used. Results MBC patients (N=40), median age 55 (rang 34–80), 58% sarcopenic, median SMG 1296 AU (SD 522). Grade 3–4 toxicity was found in 57% of sarcopenic vs 18% of non-sarcopenic patients (p=0.02). Toxicity-related hospitalizations were also higher in sarcopenic patients (39% vs 0%, p=0.005) as were any adverse events -- defined as any grade 3–4 toxicities, hospitalizations, dose reductions, or dose delay -- (74% vs 35%, p=0.02). Low SMG was associated with grade 3–4 toxicity (p=0.04), hospitalization (p=0.01) and time to treatment failure (for progression or toxicity) (p=0.03). Low SMG had a borderline significant association with any adverse event (p=0.06) and overall survival (p=0.07). Conclusions SM measures are associated with toxicity outcomes and survival in MBC patients receiving first line taxane-based chemotherapy. Further studies are needed to explore how routinely obtained CT scans can be used to individualize dosing and improve treatment planning.

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Section: Methodsmentioning

confidence: 99%

Skeletal Muscle Measures as Predictors of Toxicity, Hospitalization, and Survival in Patients with Metastatic Breast Cancer Receiving Taxane-Based Chemotherapy

Shachar

Deal

Weinberg

et al. 2017

Clinical Cancer Research

229

192

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“…Using Impac radiological software (Mountain View, CA), transverse sections at the L3 vertebral level were identified and extracted for external analysis. Automated image segmentation software was then used to analyze the L3 lumbar segments [28, 29]. The software recognizes muscle tissue based on density thresholds between −29 and +150 Hounsfield Units (HU) and provides an unbiased estimation of the cross-sectional skeletal muscle area.…”

Section: Methodsmentioning

confidence: 99%

Skeletal muscle measures and physical function in older adults with cancer: sarcopenia or myopenia?

Williams

Deal²,

Muss

et al. 2017

Oncotarget

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BackgroundSkeletal muscle loss, commonly known as sarcopenia, is highly prevalent in older adults and linked with adverse outcomes in cancer, yet the definition and role of sarcopenia remains uncertain. The aim of this study was to examine the association of Computerized Tomography (CT) assessed skeletal muscle measures with physical function in older adults with cancer.ResultsCTs for 185 patients were available. Median age 73 (IQR 68–76) and 56.5% female. After controlling for sex and BMI, we found no evidence that SMI was associated with physical function impairments. Both SMD and SMG were associated physical function impairments and higher values were associated with decreased limitations in instrumental activities of daily living (RR 0.84 [CI 0.73–0.96] and 0.94 [CI 0.89–0.99], respectively), climbing stairs (RR 0.84 [CI 0.76–0.94] and 0.91 [CI 0.87–0.96]), walking 1 block (RR 0.77 [CI 0.67–0.90] and 0.91 [CI 0.85–0.97]), and prolonged Timed Up and Go (RR 0.83 [CI 0.75–0.92] and 0.92 [CI 0.88–0.96]).Materials and MethodsUsing the Carolina Senior Registry, we identified patients with CT imaging performed within 60 days +/− of baseline geriatric assessment (GA). Skeletal muscle area and density (SMD) were analyzed from L3 lumbar segments. Muscle area and height (m2) were used to calculate skeletal muscle index (SMI). Skeletal Muscle Gauge (SMG) was created by multiplying SMI x SMD.ConclusionsSkeletal muscle mass as assessed from CT imaging was not associated with physical function impairments. Skeletal muscle radiodensity was more associated with physical function and may aid in identifying older adults at risk for functional impairments.

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“…[3]. The region that locates inside the inner wall surface with HU [−190,−30] is extracted as VAT, and the region resides outside inner wall surface within the body mask with HU [−190,−30] is extract as SAT.…”

Section: Methodsmentioning

confidence: 99%

“…For fat segmentation, the Hounsfield unit (HU) intensity is typically used to distinguish muscle and fat on CT images when performing the manual segmentation (e.g., [−29, 150] for muscle tissue and [−190, −30] for fat tissue [3]). Importantly, the compartment in which adipose tissue resides relates to the clinical significance of that fat.…”

Section: Introductionmentioning

confidence: 99%

“…Chung et al [7] presented a muscle segmentation method in which a thresholded binary image was warped to a mean shape prior by a Free Deformation model. Popuri et al [3] proposed a FEM-based registration model to perform template-based segmentation of skeletal muscle. Although these methods achieved high accuracy, they were based on 2D cross-sectional images taken at the 3rd lumbar vertebra (L3) or the 4th thoracic vertebra (T4) locations rather than on whole 3D volumes.…”

Section: Introductionmentioning

confidence: 99%

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Automated Characterization of Body Composition and Frailty with Clinically Acquired CT

Huo

Kong

et al. 2018

Computational Methods and Clinical Applications in Musculoskeletal Imaging

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Quantification of fat and muscle on clinically acquired CT scans is critical for determination of body composition, a key component of health. Manual tracing has been regarded as the gold standard method of body segmentation; however, manual tracing is time-consuming. Many semi-automated/automated algorithms have been proposed to avoid the manual efforts. Previous efforts largely focused on segmenting 2D cross-sectional images (e.g., at L3/T4 vertebra locations) rather than on the whole-body volume. In this paper, we propose a fully automated 3D body composition estimation framework for segmenting the muscle and fat from abdominal CT scans. The 3D whole body segmentations were reconstructed from a slice-wise multi-atlas label fusion (MALF) based framework. First, we used a low-dimensional atlas representation to estimate each class for each axial slice. Second, the abdominal wall and psoas muscle were segmented by combining MALF with active shape models and deformable models. Third, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were measured to assess the areas of muscle and fat tissue. The proposed method was compared to manual segmentation and demonstrated high accuracy. Then, we evaluated the approach on 40 CT scans comparing the new method to a prior atlas-based segmentation method and achieved 0.854, 0.740, 0.887 and 0.933 on Dice similarity index for the skeletal muscle, psoas muscle, VAT and SAT, respectively. Compared with the baseline, our method showed significantly (p < 0.001) higher accuracy on skeletal muscle, VAT and SAT estimation.

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Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle

Cited by 115 publications

References 36 publications

Skeletal Muscle Measures as Predictors of Toxicity, Hospitalization, and Survival in Patients with Metastatic Breast Cancer Receiving Taxane-Based Chemotherapy

Skeletal Muscle Measures as Predictors of Toxicity, Hospitalization, and Survival in Patients with Metastatic Breast Cancer Receiving Taxane-Based Chemotherapy

Skeletal muscle measures and physical function in older adults with cancer: sarcopenia or myopenia?

Automated Characterization of Body Composition and Frailty with Clinically Acquired CT

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