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Liver segmentation methods can generally be applied to any modality, sequence or vascular phase.
The methods can generally be categorized into three classes: finite difference, binomial lattice methods, or Monte Carlo simulation.
The existing methods can generally be grouped into two categories, the adsorption of gases and the adsorption of polar liquids or molecules from solution.
For example, forward modeling methods can generally reproduce mineral assemblages and compositions of rocks subject to prograde or peak-stage metamorphism and have been successfully used to predict the depths at which dehydration reactions occur (e.g., Kerrick and Connolly 2001; Hacker et al. 2003).
These methods can generally be divided into genome-wide or locus-specific approaches.
Conflicts in results between the two methods can generally be attributed to cross-reaction between probes designed for similar genes on the array.
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Experiments are conducted on four real datasets, experimental results verified our theoretical analysis, and also demonstrated that our MNPLS method can generally outperform the standard PLS and other existing methods in variable selection and prediction.
Our method can generally serve for the real-time monitoring of the effects of major social shocks or events on urban economy and consumer sentiment, providing high-resolution and cost-effective measurement tools to complement traditional economic indicators.
Since the method can generally result in small-scale optimization model and fast solution, it has been used in a number of applications in realistic industrial planning and scheduling problems.
Generally, the global methods can generate saliency maps with full resolution and evenly highlighted salient regions.
To accomplish segmentations in these applications, the methods can be generally classified as region-based and edge-based techniques.
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