Date post: | 24-May-2015 |
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Technology |
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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 [email protected]
• 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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Learn More