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Related Experiment Video

Updated: Jun 21, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

LapTrack: linear assignment particle tracking with tunable metrics.

Yohsuke T Fukai¹, Kyogo Kawaguchi^1,2,3

¹Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan.

Bioinformatics (Oxford, England)

|December 10, 2022

Summary

This summary is machine-generated.

LapTrack enhances particle tracking by enabling custom cost functions and parallel parameter tuning for improved cellular lineage construction. This tool optimizes tracking accuracy with diverse datasets and preserves ground truth connections.

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Last Updated: Jun 21, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

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Published on: February 27, 2016

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Published on: January 3, 2018

Area of Science:

Biophysics
Computational Biology
Image Analysis

Background:

Particle tracking is crucial for cellular lineage construction from live imaging.
Existing methods, often based on linear assignment problems (LAP), require parameter tuning and struggle with data diversity.
Current implementations lack flexibility for custom costs and ground truth preservation.

Purpose of the Study:

To develop a flexible and efficient particle tracking tool.
To enable custom cost functions and parallel parameter tuning with ground truth annotations.
To improve the accuracy and applicability of particle tracking for cellular lineage analysis.

Main Methods:

Developed LapTrack, a Python package implementing a LAP-based tracker.
Incorporated arbitrary cost functions and inputs for tracking.
Enabled parallel parameter tuning and ground truth track preservation.

Main Results:

LapTrack demonstrated improved tracking scores using custom metric functions compared to distance-only methods.
The tool effectively handles diverse real and artificial datasets.
LapTrack integrates seamlessly with other Python-based tools for particle analysis.

Conclusions:

LapTrack offers a flexible and powerful solution for particle tracking.
The customizability of LapTrack enhances tracking accuracy and broadens its applicability.
This tool facilitates more robust cellular lineage construction and analysis.