Automated segmentation of visceral and subcutaneous (deep and superficial) adipose tissues in normal and overweight men : Automated Segmentation of Adipose Tissue

Visceral adipose tissue (VAT) compartments may confer increased metabolic risk. The incremental utility of measuring both visceral and subcutaneous abdominal adipose tissue (SAT) in association with metabolic risk factors and underlying heritability has not been well described in a population-based setting. Participants (n=3001) were drawn from the Framingham Heart Study (48% women; mean age, 50 years), were free of clinical cardiovascular disease, and underwent multidetector computed tomography assessment of SAT and VAT volumes between 2002 and 2005. Metabolic risk factors were examined in relation to increments of SAT and VAT after multivariable adjustment. Heritability was calculated using variance-components analysis. Among both women and men, SAT and VAT were significantly associated with blood pressure, fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol and with increased odds of hypertension, impaired fasting glucose, diabetes mellitus, and metabolic syndrome (P range < 0.01). In women, relations between VAT and risk factors were consistently stronger than in men. However, VAT was more strongly correlated with most metabolic risk factors than was SAT. For example, among women and men, both SAT and VAT were associated with increased odds of metabolic syndrome. In women, the odds ratio (OR) of metabolic syndrome per 1-standard deviation increase in VAT (OR, 4.7) was stronger than that for SAT (OR, 3.0; P for difference between SAT and VAT < 0.0001); similar differences were noted for men (OR for VAT, 4.2; OR for SAT, 2.5). Furthermore, VAT but not SAT contributed significantly to risk factor variation after adjustment for body mass index and waist circumference (P < or = 0.01). Among overweight and obese individuals, the prevalence of hypertension, impaired fasting glucose, and metabolic syndrome increased linearly and significantly across increasing VAT quartiles. Heritability values for SAT and VAT were 57% and 36%, respectively. Although both SAT and VAT are correlated with metabolic risk factors, VAT remains more strongly associated with an adverse metabolic risk profile even after accounting for standard anthropometric indexes. Our findings are consistent with the hypothesized role of visceral fat as a unique, pathogenic fat depot. Measurement of VAT may provide a more complete understanding of metabolic risk associated with variation in fat distribution.

After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.

Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.