Fat Segmentation Research Articles

Improving Reliability of pQCT-Derived Muscle Area and Density Measures Using a Watershed Algorithm for Muscle and Fat Segmentation

In peripheral quantitative computed tomography scans of the calf muscles, segmentation of muscles from subcutaneous fat is challenged by muscle fat infiltration. Threshold-based edge detection segmentation by manufacturer software fails when muscle boundaries are not smooth. This study compared the test-retest precision error for muscle-fat segmentation using the threshold-based edge detection method vs manual segmentation guided by the watershed algorithm. Three clinical populations were investigated: younger adults, older adults, and adults with spinal cord injury (SCI). The watershed segmentation method yielded lower precision error (1.18%–2.01%) and higher (p < 0.001) muscle density values (70.2 ± 9.2 mg/cm3) compared with threshold-based edge detection segmentation (1.77%–4.06% error, 67.4 ± 10.3 mg/cm3). This was particularly true for adults with SCI (precision error improved by 1.56% and 2.64% for muscle area and density, respectively). However, both methods still provided acceptable precision with error well under 5%. Bland-Altman analyses showed that the major discrepancies between the segmentation methods were found mostly among participants with SCI where more muscle fat infiltration was present. When examining a population where fatty infiltration into muscle is expected, the watershed algorithm is recommended for muscle density and area measurement to enable the detection of smaller change effect sizes.

Investigating individual preferences in rating and ranking conjoint experiments. A case study on semi-hard cheese

Stated preference conjoint experiments and self-explicated measures based on rating and ranking approaches were conducted to investigate Norwegian consumers’ choices among healthier and organically produced semi-hard cheeses. In the conjoint experiments, one group of participants (n=114) performed a rating task of eight cheeses whereas the other group (n=105) performed a ranking task of the same cheeses, all based on pictorial stimuli only. Then, all participants performed self-explicated rating and ranking evaluations of the cheese attributes. Conjoint rating data were analysed by mixed model ANOVA, while conjoint ranking data were analysed by mixed logit. The different approaches are compared in terms of data analysis methodologies, outcomes and practicalities for the experimenter as well as for the respondents. Rather than average population effects, focus is brought on individual preferences and consumer segmentation. Findings reveal that the two conjoint experiments lead to similar population effects and consumer segments. Consumers on average prefer cheeses of new (healthier) fat composition, organic production and lower price to cheeses of regular fat composition, conventional production and higher price. Two consumer segments are investigated. Consumers in the New fat segment are health-conscious, whereas consumers in the Regular fat segment are attracted by conventional cheese and lower prices. Self-explicated ratings of the cheese attributes corroborate these findings.

Whole‐body MRI‐based fat quantification: A comparison to air displacement plethysmography

To demonstrate the feasibility of an algorithm for MRI whole-body quantification of internal and subcutaneous fat and quantitative comparison of total adipose tissue to air displacement plethysmography (ADP). For comparison with ADP, whole-body MR data of 11 volunteers were obtained using a continuously moving table Dixon sequence. Resulting fat images were corrected for B1 related intensity inhomogeneities before fat segmentation. The performed MR measurements of the whole body provided a direct comparison to ADP measurements. The segmentation of subcutaneous and internal fat in the abdomen worked reliably with an accuracy of 98%. Depending on the underlying model for fat quantification, the resultant MR fat masses represent an upper and a lower limit for the true fat masses. In comparison to ADP, the results were in good agreement with ρ ≥ 0.97, P < 0.0001. Whole-body fat quantities derived noninvasively by using a continuously moving table Dixon acquisition were directly compared with ADP. The accuracy of the method and the high reproducibility of results indicate its potential for clinical applications.

Bias atlases for segmentation-based PET attenuation correction using PET-CT and MR.

This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.

Combined PET/MR imaging in neurology: MR-based attenuation correction implies a strong spatial bias when ignoring bone

AimCombined PET/MR systems have now become available for clinical use. Given the lack of integrated standard transmission (TX) sources in these systems, attenuation and scatter correction (AC) must be performed using the available MR-images. Since bone tissue cannot easily be accounted for during MR-AC, PET quantification can be biased, in particular, in the vicinity of the skull. Here, we assess PET quantification in PET/MR imaging of patients using phantoms and patient data. Materials and methodsNineteen patients referred to our clinic for a PET/CT exam as part of the diagnostic evaluation of suspected dementia were included in our study. The patients were injected with 200MBq [18F]FDG and imaged with PET/CT and PET/MR in random sequence within 1h. Both, PET/CT and PET/MR were performed as single-bed acquisitions without contrast administration. PET/CT and PET/MR data were reconstructed following CT-based and MR-based AC, respectively. MR-AC was performed based on: (A) standard Dixon-Water–Fat segmentation (DWFS), (B) DWFS with co-registered and segmented CT bone values superimposed, and (C) with co-registered full CT-based attenuation image. All PET images were reconstructed using AW-OSEM, with neither resolution recovery nor time-of-flight option employed. PET/CT (D) or PET/MR (A–C) images were decay-corrected to the start time of the first examination. PET images following AC were evaluated visually and quantitatively using 10 homeomorphic regions of interest drawn on a transaxial T1w-MR image traversing the central basal ganglia. We report the relative difference (%) of the mean ROI values for (A)–(C) in reference to PET/CT (D).In a separate phantom experiment a 2L plastic bottle was layered with approximately 12mm of Gypsum plaster to mimic skull bone. The phantom was imaged on PET/CT only and standard MR-AC was performed by replacing hyperdense CT attenuation values corresponding to bone (plaster) with attenuation values of water. PET image reconstruction was performed with CT-AC (D) and CT-AC using the modified CT images corresponding to MR-AC using DWFS (A). ResultsPET activity values in patients following MR-AC (A) showed a substantial radial dependency when compared to PET/CT. In all patients cortical PET activity was lower than the activity in the central region of the brain (10–15%). When adding bone attenuation values to standard MR-AC (B and C) the radial gradient of PET activity values was removed.Further evaluation of PET/MR activity following MR-AC (A) relative to MR-AC (C) using the full CT for attenuation correction showed an underestimation of 25% in the cortical regions and 5–10% in the central regions of the brain.Observations in patients were replicated by observations from the phantom study. ConclusionOur phantom and patient data demonstrate a spatially varying bias of the PET activity in PET/MR images of the brain when bone tissue is not accounted for during attenuation correction. This has immediate implications for PET/MR imaging of the brain. Therefore, refinements to existing MR-AC methods or alternative strategies need to be found prior to adopting PET/MR imaging of the brain in clinical routine and research.

Automatic quantification of epicardial fat volume on non‐enhanced cardiac CT scans using a multi‐atlas segmentation approach

There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans. Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability. Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 ± 2.6% with respect to Observer 1 and 89.2 ± 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearson's correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 ± 2.4% for segmenting the pericardium and a Pearson's correlation coefficient of 0.92 (P<0.001) for computation of the epicardial fat volume. The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.

Local noise estimation in low-dose chest CT images

Image noise in computed tomography (CT) images may have significant local variation due to tissue properties, dose, and location of the X-ray source. We developed and tested an automated tissue-based estimator method for estimating local noise in CT images. An automated TBE method for estimating the local noise in CT image in 3 steps was developed: (1) Partition the image into homogeneous and transition regions, (2) For each pixel in the homogeneous regions, compute the standard deviation in a 15 x 15 x 1 voxel local region using only pixels from the same homogeneous region, and (3) Interpolate the noise estimate from the homogeneous regions in the transition regions. Noise-aware fat segmentation was implemented. Experiments were conducted on the anthropomorphic phantom and in vivo low-dose chest CT scans to validate the TBE, characterize the magnitude of local noise variation, and determine the sensitivity of noise estimates to the size of the region in which noise is computed. The TBE was tested on all scans from the Early Lung Cancer Action Program public database. The TBE was evaluated quantitatively on the phantom data and qualitatively on the in vivo data. The results show that noise can vary locally by over 200 Hounsfield units on low-dose in vivo chest CT scans and that the TBE can characterize these noise variations within 5 %. The new fat segmentation algorithm successfully improved segmentation on all 50 scans tested. The TBE provides a means to estimate noise for image quality monitoring, optimization of denoising algorithms, and improvement of segmentation algorithms. The TBE was shown to accurately characterize the large local noise variations that occur due to changes in material, dose, and X-ray source location.

THU0372 MRI quantification of fat infiltration in skeletal muscle of patients with camptocormia

BackgroundPrimitive progressive lumbar paravertebral muscle weakness commonly known as Primitive Camptocormia and also referred to as “bent spine syndrome” is characterized by an abnormal trunk posture with marked flexion of...

THU0478 MRI Quantitative Analysis of Fatty Infiltration of Paravertebral Muscles in Patients with Primary Camptocormia

BackgroundCamptocormia also referred to as “bent spine syndrome” is characterized by an abnormal trunk posture with marked flexion of the thoraco-lumbar spine. These clinical signs are most prominent on standing...

Vitamin D status is related to intramyocellular lipid in older adults

Previous work has recognized that vitamin D status is inversely associated with extramyocellular lipid (EMCL). However, the relationship between vitamin D and intramyocellular lipid (IMCL) has not been established, particularly in aged subjects. Since IMCL and vitamin D status are both associated with muscle function, our objective was to further examine these relationships in an elderly sample. Measures of 25(OH)D were obtained in 13 adults between the ages of 65–78. Lateral and medial gastrocnemius IMCL and EMCL were measured with magnetic resonance spectroscopy and fat segmentation. A lifestyle questionnaire assessed physical activity level. Strength (1‐rep max) and physical performance measures (step test/gait speed) were performed. Mean body mass index (BMI) was 26.5±1.1kg/m2 with a range of 19.5 to 33.7kg/m2. Mean 25(OH)D was 37.9±4.1ng/mL with a range of 19 to 68ng/mL. IMCL to water ratio was 0.53±0.06. 25(OH)D correlated with IMCL (r=0.39; p=0.02), but not with EMCL (r=0.01; p=0.94). IMCL was not correlated with BMI (r=0.03; p=0.59) or different by physical activity level (p=0.37). Strength and performance were not correlated with IMCL (p&gt;;0.4), EMCL (p&gt;;0.7) or 25(OH)D (p&gt;;0.09). These data suggest that vitamin D status is positively associated with IMCL. Future research should examine the vitamin D and muscle lipid relationship to determine if vitamin D contributes to [IMCL]/[EMCL] repartitioning.

Comparison of 3‐point dixon imaging and fuzzy C‐means clustering methods for breast density measurement

To assess two methods of fat and fibroglandular tissue (FGT) segmentation for measuring breast MRI FGT volume and FGT%, the volume percentage of FGT in the breast, in longitudinal studies. Nine premenopausal women provided one MRI per week for 4 weeks during a natural menstrual cycle for a total of 36 datasets. We compared a fuzzy c-means (FC) and a 3-point Dixon segmentation method for estimation of changes in FGT volume and FGT% across the menstrual cycle. We also assessed whether differences due to changes in positioning each week could be minimized by coregistration, i.e., the application of the breast boundary selected at one visit to images obtained at other visits. FC and Dixon FGT volume were highly correlated (r = 0.93, P < 0.001), as was FC and Dixon FGT% (r = 0.86, P = 0.01), although Dixon measurements were on average 10-20% higher. Although FGT measured by both methods showed the expected pattern of increase during the menstrual cycle, the magnitude, and for one woman the direction, of change varied according to the method used. Measurements of FGT for coregistered images were in close agreement with those for which the boundaries were determined independently. The method of segmentation of fat and FGT tissue may have an impact on the results of longitudinal studies of changes in breast MRI FGT.

Automated unsupervised multi‐parametric classification of adipose tissue depots in skeletal muscle

To introduce and validate an automated unsupervised multi-parametric method for segmentation of the subcutaneous fat and muscle regions to determine subcutaneous adipose tissue (SAT) and intermuscular adipose tissue (IMAT) areas based on data from a quantitative chemical shift-based water-fat separation approach. Unsupervised standard k-means clustering was used to define sets of similar features (k = 2) within the whole multi-modal image after the water-fat separation. The automated image processing chain was composed of three primary stages: tissue, muscle, and bone region segmentation. The algorithm was applied on calf and thigh datasets to compute SAT and IMAT areas and was compared with a manual segmentation. The IMAT area using the automatic segmentation had excellent agreement with the IMAT area using the manual segmentation for all the cases in the thigh (R(2): 0.96) and for cases with up to moderate IMAT area in the calf (R(2): 0.92). The group with the highest grade of muscle fat infiltration in the calf had the highest error in the inner SAT contour calculation. The proposed multi-parametric segmentation approach combined with quantitative water-fat imaging provides an accurate and reliable method for an automated calculation of the SAT and IMAT areas reducing considerably the total postprocessing time.

Automatic intra‐subject registration‐based segmentation of abdominal fat from water–fat MRI

To develop an automatic registration-based segmentation algorithm for measuring abdominal adipose tissue depot volumes and organ fat fraction content from three-dimensional (3D) water-fat MRI data, and to evaluate its performance against manual segmentation. Data were obtained from 11 subjects at two time points with intermediate repositioning, and from four subjects before and after a meal with repositioning. Imaging was performed on a 3 Tesla MRI, using the IDEAL chemical-shift water-fat pulse sequence. Adipose tissue (subcutaneous--SAT, visceral--VAT) and organs (liver, pancreas) were manually segmented twice for each scan by a single trained observer. Automated segmentations of each subject's second scan were generated using a nonrigid volume registration algorithm for water-fat MRI images that used a b-spline basis for deformation and minimized image dissimilarity after the deformation. Manual and automated segmentations were compared using Dice coefficients and linear regression of SAT and VAT volumes, organ volumes, and hepatic and pancreatic fat fractions (HFF, PFF). Manual segmentations from the 11 repositioned subjects exhibited strong repeatability and set performance benchmarks. The average Dice coefficients were 0.9747 (SAT), 0.9424 (VAT), 0.9404 (liver), and 0.8205 (pancreas); the linear correlation coefficients were 0.9994 (SAT volume), 0.9974 (VAT volume), 0.9885 (liver volume), 0.9782 (pancreas volume), 0.9996 (HFF), and 0.9660 (PFF). When comparing manual and automated segmentations, the average Dice coefficients were 0.9043 (SAT volume), 0.8235 (VAT), 0.8942 (liver), and 0.7168 (pancreas); the linear correlation coefficients were 0.9493 (SAT volume), 0.9982 (VAT volume), 0.9326 (liver volume), 0.8876 (pancreas volume), 0.9972 (HFF), and 0.8617 (PFF). In the four pre- and post-prandial subjects, the Dice coefficients were 0.9024 (SAT), 0.7781 (VAT), 0.8799 (liver), and 0.5179 (pancreas); the linear correlation coefficients were 0.9889, 0.9902 (SAT, and VAT volume), 0.9523 (liver volume), 0.8760 (pancreas volume), 0.9991 (HFF), and 0.6338 (PFF). Automated intra-subject registration-based segmentation is potentially suitable for the quantification of abdominal and organ fat and achieves comparable quantitative endpoints with respect to manual segmentation.

Novel segmentation method for abdominal fat quantification by MRI

To introduce and describe the feasibility of a novel method for abdominal fat segmentation on both water-saturated and non-water-saturated MR images with improved absolute fat tissue quantification. A general fat distribution model which fits both water-saturated (WS) and non-water-saturated (NWS) MR images based on image gray-level histogram is first proposed. Next, a novel fuzzy c-means clustering step followed by a simple thresholding is proposed to achieve automated and accurate abdominal quantification taking into consideration the partial-volume effects (PVE) in abdominal MR images. Eleven subjects were scanned at central abdomen levels with both WS and NWS MRI techniques. Synthesized "noisy" NWS (nNWS) images were also generated to study the impact of reduced SNR on fat quantification using the novel approach. The visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) amounts of the WS MR images were quantified with a traditional intensity thresholding method as a reference to evaluate the performance of the novel method on WS, NWS, and nNWS MR images. The novel approach resulted in consistent SAT and VAT amounts for WS, NWS, and nNWS images. Automatic segmentation and incorporation of spatial information during segmentation improved speed and accuracy. These results were in good agreement with those from the WS images quantified with a traditional intensity thresholding method and accounted for PVE contributions. The proposed method using a novel fuzzy c-means clustering method followed by thresholding can achieve consistent quantitative results on both WS and NWS abdominal MR images while accounting for PVE contributing inaccuracies.

Adipose segmentation in small animals at 7T: a preliminary study

BackgroundSmall animal MRI at 7 Tesla (T) provides a useful tool for adiposity research. For adiposity researchers, separation of fat from surrounding tissues and its subsequent quantitative or semi- quantitative analysis is a key task. This is a relatively new field and a priori it cannot be known which specific biological questions related to fat deposition will be relevant in a specific study. Thus it is impossible to predict what accuracy and what spatial resolution will be required in all cases and even difficult what accuracy and resolution will be useful in most cases. However the pragmatic time constraints and the practical resolution ranges are known for small animal imaging at 7T. Thus we have used known practical constraints to develop a method for fat volume analysis based on an optimized image acquisition and image post processing pair.MethodsWe designed a fat segmentation method based on optimizing a variety of factors relevant to small animal imaging at 7T. In contrast to most previously described MRI methods based on signal intensity of T1 weighted image alone, we chose to use parametric images based on Multi-spin multi-echo (MSME) Bruker pulse sequence which has proven to be particularly robust in our laboratory over the last several years. The sequence was optimized on a T1 basis to emphasize the signal. T2 relaxation times can be calculated from the multi echo data and we have done so on a pixel by pixel basis for the initial step in the post processing methodology. The post processing consists of parallel paths. On one hand, the weighted image is precisely divided into different regions with optimized smoothing and segmentation methods; and on the other hand, a confidence image is deduced from the parametric image according to the distribution of relaxation time relationship of typical adipose. With the assistance of the confidence image, a useful software feature was implemented to which enhances the data and in the end results in a more reliable and flexible method for adipose evaluation.ResultsIn this paper, we describe how we arrived at our recommended procedures and key aspects of the post-processing steps. The feasibility of the proposed method is tested on both simulated and real data in this preliminary research. A research tool was created to help researchers segment out fat even when the anatomical information is of low quality making it difficult to distinguish between fat and non-fat. In addition, tool is designed to allow the operator to make adjustments to many of the key steps for comparison purposes and to quantitatively assess the difference these changes make. Ultimately our flexible software lets the researcher define key aspects of the fat segmentation and quantification.ConclusionsCombining the full T2 parametric information with the optimized first echo image information, the research tool enhances the reliability of the results while providing more flexible operations than previous methods. The innovation in the method is to pair an optimized and very specific image acquisition technique to a flexible but tuned image post processing method. The separation of the fat is aided by the confidence distribution of regions produced on a scale relevant to and dictated by practical aspects of MRI at 7T.

Assessing beef carcass tissue weights using computed tomography spirals of primal cuts

More than 800 beef primal cuts from 44 Aberdeen Angus and Limousin-cross steers carcasses were scanned using spiral computed tomography (CT) and dissected. Thresholds for the segmentation of fat, muscle and bone in the CT spirals were estimated with the objective of assessing the weight of these tissues in the primal cuts and in the entire carcasses. Thresholds were estimated using half of the dataset (DBE) and then validated in the other half (DBV). Automatic image analysis procedures were used to assess tissue weights. The R 2 of the regression between primal tissue weight by dissection and CT were high in both datasets for fat (DBE, 0.89; DBV, 0.92), muscle (DBE, 0.99; DBV, 0.99) and bone (DBE, 0.95; DBV, 0.97). The estimation of total carcass tissue weights were also very accurate for the three tissues ( R 2 values of 0.95 to 0.96), indicating that CT scanning may deliver very accurate information on beef carcass composition faster and with lower cost than physical dissection and without damaging or depreciating the primal joints.

Uniform Fat Segment and Cusp Properties for Compactness in Shape Optimization

A general compactness theorem for shape/geometric analysis and optimization is given for a family of subsets verifying the uniform fat segment property in a bounded open holdall with or without constraints on the De Giorgi [11] or the γ-density perimeter of Bucur and Zolesio [3]. The uniform fat segment property is shown to be equivalent to the uniform cusp property introduced in [9] with a continuous non-negative cusp function. This equivalence remains true for cusp functions that are only continuous at the origin. The equivalence of sets verifying a segment property with their C0-graph representation is further sharpened for sets with a compact boundary. Our C0-graph characterization is shown to be equivalent to both the uniform cusp property and the uniform segment property. It is used to formulate sufficient conditions on the local graphs of a family of subsets of a bounded open holdall to get compactness. A first condition assumes that the local graphs are bounded above by a cusp function; a second condition which requires that the local graphs be equicontinuous turns out to be equivalent to the first one. The respective solutions of the Laplacian with homogeneous Dirichlet or Neumann boundary condition are shown to be continuous with respect to domains in that family. In the Dirichlet case for 1 < p < ∞, we prove the (1,p)-stability of compact sets in the sense of Herdberg [14] under the weaker almost everywhere assumption rather than quasi everywhere. It is also shown that for the family of measurable crack free sets \(\Omega\) in a bounded open holdall \(D\colon \ v\in \{w\in W^{1,p}_0(D)\colon \ w= 0 \mbox{ almost everywhere on } D\backslash \Omega\}\) implies \(v|_{{\rm int} {\Omega}}\in W^{1,p}_0({\rm int} {\Omega})\).

Segmentation of fascias, fat and muscle from magnetic resonance images in humans: the DISPIMAG software

Segmentation of human limb MR images into muscle, fat and fascias remains a cumbersome task. We have developed a new software (DISPIMAG) that allows automatic and highly reproducible segmentation of lower-limb MR images. Based on a pixel intensity analysis, this software does not need any previous mathematical or statistical assumptions. It displays a histogram with two main signals corresponding to fat and muscle, and permits an accurate quantification of their relative spatial distribution. To allow a systematic discrimination between muscle and fat in any subject, fixed boundaries were first determined manually in a group of 24 patients. Secondly, an entirely automatic process using these boundaries was tested by three operators on four patients and compared to the manual approach, showing a high concordance.

Segmentation of fat and muscle from MR images of the thigh by a possibilistic clustering algorithm

Physical training is proved to induce changes in physical capacity and body composition. We propose in this article a fast, unsupervised and fully three-dimensional automatic method to extract muscle and fat volumes from magnetic resonance images of thighs in order to assess these changes. The technique relies on the use of a fuzzy clustering algorithm and post-processings to trustfully process the body composition of thighs. Results are compared on 11 healthy voluntary elderly people with those provided on the same data by a validated method already published, and its reliability is assessed on repeated measures on three subjects. The two methods statistically agree when computing muscle and fat volumes, and clinical implications of this fully automatic method are important for medicine, physical conditioning, weight-loss programs and predictions of optimal body weight.

Growth Hormone and Dexamethasone Stimulate Lipolysis and Activate Adenylyl Cyclase in Rat Adipocytes by Selectively Shifting Gi 2 to Lower Density Membrane Fractions

GH, in the presence of glucocorticoid, produces a delayed increase in lipolysis in rat adipose tissue, but the biochemical mechanisms that account for this action have not been established. Other lipolytic agents rapidly activate adenylyl cyclase (AC) and the resulting production of cAMP initiates a chain of reactions that culminates in the activation of hormone-sensitive lipase. We compared responses of segments of rat epididymal fat or isolated adipocytes to 30 ng/ml GH and 0.1 μg/ml dexamethasone (Dex) with 0.1 ng/ml isoproterenol (ISO), which evoked a similar increase in lipolysis. All measurements were made during the fourth hour after the addition of GH+Dex or immediately after the addition of ISO to cells or tissues that had been preincubated for 3 h without hormone. Although no significant increases in cAMP were discernible in homogenates of GH+Dex-treated tissues, RP-cAMPS (RP-adenosine 3′5′-phosphothioate), a competitive inhibitor of cAMP, was equally effective in decreasing lipolysis induced by GH+Dex or ISO. The proportion of PKA that was present in the active form was determined by measuring the incorporation of 32P from[γ -32P]ATP into kemptide in the absence and presence of saturating amounts of cAMP. GH+Dex and ISO produced similar increases in protein kinase A activity in tissue extracts. Treatment with GH+Dex did not change the total forskolin-stimulated AC present in either a crude membrane pellet sedimented at 16K × g or a less dense membrane pellet sedimented at 100K × g, but doubled the AC activity in the 16K pellet when assayed in the absence of forskolin. To evaluate possible effects on G proteins, pellets obtained from centrifugation of adipocyte homogenates at 16K × g and 100K × g were solubilized and subjected to PAGE and Western analysis. GH+Dex decreased Giα2 by 44% (P < 0.02) in the 16K pellets and increased it by 52% (P < 0.01) in the 100K pellets. Gsα in the 16K pellet was unaffected by GH+Dex and was decreased (P < 0.05) in the 100K pellet. Sucrose density fractionation of the 16K pellets revealed a similar GH+Dex-dependent shift of Giα2 to less dense fractions as determined by both Western analysis and[ 32P]NAD ribosylation catalyzed by pertussis toxin. No such changes were seen in the distribution of Gsα or 5′-nucleotidase. Colchicine (100 μm) blocked the GH+Dex-dependent shift of Giα2 from the 16K to the 100K pellet and blocked the lipolytic effects of GH+Dex, but not those of ISO. We conclude that by modifying the relationship between AC and Giα2, GH+Dex relieves some inhibition of cAMP production and consequently increases lipolysis.