Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A lightweight network for segmenting tree-like structures in medical images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same author

Multimodal animal health monitoring in extensive livestock production systems.

Frontiers in veterinary science·2026

Same author

Cell segmentation in microscopy images using a SAM-based U-Net architecture and a novel dataset.

Computer methods and programs in biomedicine·2026

Same author

OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.

Scientific reports·2026

Same author

Medical hierarchical image classification via dual-geometry image-text learning.

Medical image analysis·2026

Same author

Multiple attention based deep multimodal fusion network for glaucoma and neurodegenerative disease diagnosis.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Jan 3, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Particle Mobility Analysis Using Deep Learning and the Moment Scaling Spectrum.

Marloes Arts^1,2, Ihor Smal^3,4,5, Maarten W Paul⁶

¹Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. marloes.e.arts@gmail.com.

Scientific Reports

|November 22, 2019

Summary

This study introduces a novel deep learning method for analyzing particle movement in cells. It segments trajectories to reveal distinct mobility patterns, enhancing our understanding of cellular dynamics.

More Related Videos

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

Related Experiment Videos

Last Updated: Jan 3, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

Area of Science:

Cell biology
Biophysics
Computational biology

Background:

Quantitative analysis of dynamic cellular processes relies on accurate particle tracking and parameter extraction from microscopy data.
Existing methods for particle tracking are abundant, but robust solutions for subsequent trajectory analysis and parameter extraction remain limited.

Purpose of the Study:

To present a novel deep learning-based method for segmenting single particle trajectories into consistent tracklets.
To utilize moment scaling spectrum analysis on tracklets for estimating mobility classes and their parameters.
To provide a robust tool for extracting biologically relevant parameters from particle trajectories in live-cell imaging.

Main Methods:

Deep learning approach for segmenting single particle trajectories into motion-consistent tracklets.
Moment scaling spectrum analysis applied to tracklets to characterize particle motion.
Validation using in-house and publicly available particle tracking datasets.

Main Results:

Successful segmentation of single particle trajectories into tracklets representing distinct motion behaviors.
Accurate estimation of the number of mobility classes and their associated parameters.
Demonstrated broad applicability across various proteins with different dynamic behaviors.

Conclusions:

The developed deep learning method offers a robust solution for analyzing particle dynamics in living cells.
This approach enhances the extraction of fundamental knowledge about particle behavior from microscopy data.
The method shows broad applicability and potential for advancing quantitative cell biology research.