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Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Three-Compartment Open Model01:06

Three-Compartment Open Model

The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
Two-Compartment Open Model: Overview01:05

Two-Compartment Open Model: Overview

Multicompartmental models are crucial tools in pharmacokinetics, providing a framework to understand how drugs move within the body. The two-compartment model is a crucial subtype, segmenting the body into central and peripheral compartments. The central compartment represents areas with high blood flow, such as plasma and highly perfused organs like the kidneys and liver, while the peripheral compartment signifies tissues with lower blood flow, like adipose tissue and muscle tissue.
The...

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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A Multi-Compartment Segmentation Framework With Homeomorphic Level Sets.

Xian Fan1, Pierre-Louis Bazin, Jerry L Prince

  • 1Johns Hopkins University, Baltimore MD 21218.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for simultaneous multi-object segmentation, efficiently handling complex relationships and topology. The method applies object-dependent forces, enabling precise segmentation without overlaps or gaps.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Imaging

Background:

  • Simultaneous multi-object segmentation is crucial for various imaging applications.
  • Existing level set methods struggle with maintaining object relationships, topology, and object-dependent forces.
  • A need exists for efficient and robust multi-object segmentation techniques.

Purpose of the Study:

  • To present a novel framework for simultaneous multi-object segmentation.
  • To enable object-dependent force application while preserving topology and relationships.
  • To achieve computationally efficient segmentation of numerous objects.

Main Methods:

  • Developed a framework for simultaneous multi-object segmentation.
  • Incorporated object-dependent internal and external forces.
  • Ensured maintenance of object topology and relationships, preventing overlaps and vacuums.

Main Results:

  • The framework successfully segments multiple objects, including those with multiple compartments.
  • It maintains object topology and relationships without generating overlaps or vacuums.
  • Computational complexity is independent of the number of segmented objects.

Conclusions:

  • The proposed framework offers a significant advancement in simultaneous multi-object segmentation.
  • It addresses limitations of existing methods by providing object-dependent forces and preserving topological integrity.
  • The approach is computationally efficient and versatile, applicable to both synthetic and real-world images.