Multi-worm tracking using superposition of merit functions

Rivera, Linda; Potsaid, Benjamin; Wen, John T.

doi:10.1117/12.816468

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…These tracking systems can be divided into two classes: single-object trackers and multi-objects trackers. Mostly, single-objects trackers may remove detailed phenotypic characteristics from objects at a high magnification [57][58][59][60][61] while multi-objects trackers are bounded to extracting movement metrics such as speed and travel path from object at a low magnification [62][63][64][65][66]. The algorithm of Worm-Tracker and applying processing of it on this study is completely explained in chapter 4.…”

Section: Worm Track-jmentioning

confidence: 99%

Fever detection for dynamic human environment using sensor fusion

Fallah-Haghmohammadi

Necsulecsu

2017

2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) &Amp; 2017 Intl Aegean Conference

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Section: Worm Track-jmentioning

confidence: 99%

Fever detection for dynamic human environment using sensor fusion

Fallah-Haghmohammadi

Necsulecsu

2017

2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) &Amp; 2017 Intl Aegean Conference

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“…These systems can be divided into two classes: single-worm trackers and multi-worm trackers. Generally, single-worm trackers may extract detailed phenotypic features from worms at a high magnification [1]–[5] whereas multi-worm trackers are limited to extracting movement metrics such as speed and travel path from worms at a low magnification [6]–[10]. These automated worm tracking systems have greatly improved ways of detecting and characterizing mutant behavior.…”

Section: Introductionmentioning

confidence: 99%

Track-A-Worm, An Open-Source System for Quantitative Assessment of C. elegans Locomotory and Bending Behavior

Wang

2013

PLoS ONE

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A major challenge of neuroscience is to understand the circuit and gene bases of behavior. C. elegans is commonly used as a model system to investigate how various gene products function at specific tissue, cellular, and synaptic foci to produce complicated locomotory and bending behavior. The investigation generally requires quantitative behavioral analyses using an automated single-worm tracker, which constantly records and analyzes the position and body shape of a freely moving worm at a high magnification. Many single-worm trackers have been developed to meet lab-specific needs, but none has been widely implemented for various reasons, such as hardware difficult to assemble, and software lacking sufficient functionality, having closed source code, or using a programming language that is not broadly accessible. The lack of a versatile system convenient for wide implementation makes data comparisons difficult and compels other labs to develop new worm trackers. Here we describe Track-A-Worm, a system rich in functionality, open in source code, and easy to use. The system includes plug-and-play hardware (a stereomicroscope, a digital camera and a motorized stage), custom software written to run with Matlab in Windows 7, and a detailed user manual. Grayscale images are automatically converted to binary images followed by head identification and placement of 13 markers along a deduced spline. The software can extract and quantify a variety of parameters, including distance traveled, average speed, distance/time/speed of forward and backward locomotion, frequency and amplitude of dominant bends, overall bending activities measured as root mean square, and sum of all bends. It also plots worm travel path, bend trace, and bend frequency spectrum. All functionality is performed through graphical user interfaces and data is exported to clearly-annotated and documented Excel files. These features make Track-A-Worm a good candidate for implementation in other labs.

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Multi-worm tracking using superposition of merit functions

Cited by 2 publications

References 13 publications

Fever detection for dynamic human environment using sensor fusion

Fever detection for dynamic human environment using sensor fusion

Track-A-Worm, An Open-Source System for Quantitative Assessment of C. elegans Locomotory and Bending Behavior

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