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Related Concept Videos

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Molecular Kinetic Energy01:21

Molecular Kinetic Energy

The word "gas" comes from the Flemish word meaning "chaos," first used to describe vapors by the chemist J. B. van Helmont. Consider a container filled with gas, with a continuous and random motion of molecules. During collisions, the velocity component parallel to the wall is unchanged, and the component perpendicular to the wall reverses direction but does not change in magnitude. If the molecule’s velocity changes in the x-direction, then its momentum is changed. During the short time of the...

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Updated: Jun 28, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Kinetic modeling based probabilistic segmentation for molecular images.

Ahmed Saad1, Ghassan Hamarneh, Torsten Möller

  • 1Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada. aasaad@cs.sfu.ca

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 5, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semi-supervised segmentation technique for molecular imaging. The method improves accuracy in low signal-to-noise ratio (SNR) and partial volume effect (PVE) conditions, outperforming existing approaches.

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Area of Science:

  • Molecular imaging
  • Medical image analysis
  • Computational neuroscience

Background:

  • Molecular imaging techniques like dynamic positron emission tomography (dPET) are crucial for understanding brain function.
  • Low signal-to-noise ratio (SNR) and partial volume effect (PVE) are significant challenges in dPET image analysis.
  • Accurate segmentation of functional regions is essential for reliable kinetic parameter recovery.

Purpose of the Study:

  • To develop and validate a semi-supervised, kinetic modeling-based segmentation technique for molecular imaging.
  • To address and mitigate the detrimental effects of low SNR and PVE in dPET brain images.
  • To improve the identification of functional brain regions and the accuracy of kinetic parameter estimation.

Main Methods:

  • An iterative, self-learning algorithm employing uncertainty principles for segmentation.
  • Application of the algorithm to synthetic fluorodeoxyglucose (FDG) and simulated Raclopride dPET brain images.
  • Validation using simulated data with intentionally introduced high noise levels.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to state-of-the-art methods.
  • Qualitative and quantitative assessments confirmed improved identification of functional brain regions.
  • Enhanced recovery of kinetic parameters was achieved, even under challenging imaging conditions.

Conclusions:

  • The developed semi-supervised technique effectively segments molecular imaging data, particularly in the presence of noise and PVE.
  • This method offers a significant advancement for analyzing dPET brain imaging, leading to more accurate functional region identification and kinetic modeling.
  • The algorithm shows promise for improving diagnostic and research applications in molecular neuroimaging.