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Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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Related Experiment Video

Updated: Jun 26, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm.

Katia M Passera1, Paolo Potepan, Luca Brambilla

  • 1Dipartimento di Ingegneria Biomedica, Politecnico di Milano, Italy. katia.passera@polimi.it

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
Summary
This summary is machine-generated.

A new semi-automatic segmentation method using a Gaussian hidden Markov random field (GHMRF) model improves intestinal-type adenocarcinoma (ITAC) volume assessment accuracy. This advanced model enhances lesion area quantification for evaluating tumor response to therapy.

Related Experiment Videos

Last Updated: Jun 26, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

  • Medical imaging analysis
  • Computational pathology
  • Oncology

Background:

  • Accurate volume assessment of Intestinal-type adenocarcinoma (ITAC) is crucial for treatment evaluation.
  • Existing methods like finite Gaussian mixture (FGM) models may lack spatial information encoding for precise segmentation.
  • Advanced modeling is needed to improve the accuracy of tumor volume quantification.

Purpose of the Study:

  • To present and validate a semi-automatic segmentation method for ITAC volume assessment.
  • To introduce a Gaussian hidden Markov random field (GHMRF) model incorporating spatial context.
  • To compare the performance of GHMRF against FGM for lesion area quantification.

Main Methods:

  • Development of a semi-automatic segmentation technique based on a Gaussian hidden Markov random field (GHMRF) model.
  • Application of the expectation maximization (EM) algorithm for fitting the GHMRF model.
  • Validation using magnetic resonance imaging datasets (T1-weighted, CE T1-weighted, T2-weighted) from 49 tumor-containing slices.

Main Results:

  • The GHMRF model demonstrated higher accuracy in quantifying lesion area compared to the FGM model.
  • Numerical and clinical evaluations confirmed the superior performance of the proposed GHMRF method.
  • The method shows potential for evaluating tumor response to therapy.

Conclusions:

  • The proposed semi-automatic GHMRF segmentation method offers improved accuracy for ITAC volume assessment.
  • This technique enhances lesion quantification, aiding in the evaluation of therapeutic efficacy.
  • GHMRF-based segmentation represents a valuable advancement in oncological imaging analysis.