how wormlab works

How WormLab Tracks

• Supports high mag and low mag (whole plate) tracking

• Composed of 2 parts:• Detection (finds new worms as the enter the movie)

• Tracking (determining changes in worm position and shape from frame to frame)

• Thresholding tools to refine background and improve detection despite moderate background clutter

• Uses geometric model, worm motion model, backtracking and Multiple Hypothesis Tracking for accurate detection

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Worm Detection

• The image is inverted and segmented to identify potential worm objects

• The algorithm measures 2 points of high curvature from a closed planar B-spline curve around the boundary of the worm object

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Head and Tail Determination

• Identification based on the worm’s shape and frequency of movement

• We apply the same spatial and temporal cues used by human observers: • The worm’s tail area is lighter than the head • The worm’s tail area is thinner than the

head • The head moves more frequently than the

tail

• Head/tail identification can be swapped for entire track by user

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Detected Head

Geometric Model

• Based on the center line of the worm and boundary

• Modeled on a spline basis to allow easy scaling and resampling at different resolutions

• User can determine the # of points along the center line used in the analysis• 3 pts: head, tail, center

• 17-19 pts: bending analysis

• 59 pts: full resolution (default)

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Worm Motion Model

• ɳ = movement along centerline (peristaltic progression factor)

• Δα = Displacement orthogonal to the trajectory

• Also use elongation and contraction to model motion

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Tracking Across Frames

• A deformable model estimation algorithm fits the geometric model from the previous frame to the current frame

• Backtracking is performed to re-establish worms with their previous tracks if lost

• Backtracking used if video starts with entangled worms

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Multiple Hypothesis Tracking

• Apply a set of hypothesized worm locations across time, thus building a hypothesis tree

• Resolve conflicts by finding the path of Maximum Fitness (best fit across frames)

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Detection of Complex Behaviors

• The geometric model, worm motion model, and MHT help identify worms in ambiguous conformations:• Coiled worms, • Overlapping worms• Omega bends• Reversing worms

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Editing Functions

• Manually draw a worm that is not detected prior to tracking

• Swap head and tail across a track• Join tracks• Split tracks• Delete worms per frame or across all frames

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Metrics and Analyses

• Length: Distance between head and tail along central axis

• Width is calculated from N points along the worm• Direction is the direction of travel• Postion is the center of the median axis• Instaneous speed: Velocity along the central axis from

one frame to the next• Moving Average Speed: Instantaneous speed

averaged over multiple frames• Amplitude: Amplitude of the sine wave that best fits

the worm posture• Wavelength: Period of the sine wave the best fits the

worm’s posture • Bend Angle: Bending angle at the midpoint

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Detection of Omega Bends

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• Begins when the bending angle between head-midpoint and tail-midpoint drops below 1.57 radians ( 90°) and continues until the angle exceeds 1.57 radians

Detection of Reversals

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• Reversal is defined as worm moving backwards for user defined set of frames

Head Bending Analysis

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• Indicates foraging

• Worm sampled with 19pts

• Bending angle is 3pt from head

Imaging Suggestions

• Contrast: dark solid worms on light background• Lawn: replate worms to minimize tracks• Frame Rate: 5-10fps is adequate, faster for swimming

worms• Cameras:

• Industrial machine vision cameras (CCD) work• Webcams (low cost CMOS not so much)• Recommend monochrome cameras

• Image size: • Whole plate: 2500x2500 resolution (5 Megapixels)• Single worms: 800x600, 1200x1024 and faster frame rates

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Video provided by Dr. Kate Harwood

WormLab Overview

• PC & MAC compatible

• Accepts video files in numerous formats

• Includes data and video export (with tracking overlay)

• Workflow based – easy to train and use

• Export metrics to Matlab and Excel

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• Control camera hardware to record videos from stereoscopes, inverted microscopes, or macro photography setup• Automatic Save• Variable Frame Rate • Scaling Tool: Calculate the pixel size• Scaling and frame rate are saved within the video file, and

automatically read by WormLab for analysis• Support DCAM/IIDC compliant cameras (Point Grey, Allied

Vision and Sony)

Camera Control

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• Track swimming, thrashing worms:

• Use a modified worm motion model to map the oscillation of the center point radially

• Quantify pharyngeal pumping

• Synchronization of stimulation and tracking

• New analyses for bending and shape interpretation

• Development of different assays – chemotaxis studies, etc.

WormLab – Future Directions

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Summary

• WormLab for automatic detection and tracking of worms

• Provide metrics including size, speed, direction• Track in complex backgrounds, entanglements,

and shapes• Capture video sequences or open previously

acquired sequences

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• Email questions to Julie Korich at julie@mbfbioscience.com

• Download a free trial www.mbfbioscience.com/wormlab

• Watch a webinar that gives an overview of WormLab

www.mbfbioscience.com/webinars

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