The Detection panel

If checked, worms touching the edge of the image are included in the detection if they are valid.

Minimum/maximum area of valid worms.

Minimum/maximum length of valid worms.

Minimum/maximum width of valid worms.

Minimum/maximum width to length ratio of valid worms.

Minimal degree of fitness between newly detected worms and image (i.e., the degree to which the contour of the model aligns with the contour of the worm binary image).

Minimal degree of fitness between registered worms and image. The value is typically lower than detection fit because worm registration occurs in complex tracking scenarios (e.g., entanglement or occlusion) where the fit between model and image is usually lower.

WormLab regularly performs an image-wide detection sweep to detect new worms entering the field of view. This setting determines how often this detection process is performed. A higher number of frames between detection increases processing speed, but delays the detection of worms entering the field of view.

To restrict tracking to worms selected manually, set the frequency to 0 (zero).

Our default worm model assumes that worms maintain a constant length. Selecting this parameter activates length elasticity with the assumption that worm length is variable.

Length Fitting is useful when the change in worm length must be captured accurately or when detection accuracy of the worm extremities is insufficient.

Systematically adjusts worm width along the length of the worm. Selecting this parameter will decrease processing speed.

The whole plate tracking mode uses a different model designed for low magnification and a large field of view.

For low resolution, worms typically only occupy a few pixels in the image, so accurate worm shape or interaction interpretation is not possible. In this mode, you sacrifice accuracy and tracking robustness for an increase in processing speed.

Notably absent from this mode is tracking robustness to worm interaction and self-overlap. For example, when two worms touch one another, WormLab drops their associated tracking , resuming it only when interaction ends.

There are two sets of criteria used for detection:

• Morphometric data (used in previous versions): Head is assigned to wider extremity.
• Bending of the extremities data: Foraging behavior (side to side movement of the nose as the worm is crawling) is used to determine head.

You can use the slider to select the weight of the criteria used for detection:

• Slider near Width extremity assigns all weight to morphometric data.
• Slider near bending extremity assigns all weight to bending data.
• Slider near balanced assigns equal weight to morphometric and bending data.

The number of iterations used in the worm-fitting process. With very big worms or high-magnification images, this number may need to be increased.

You may need to increase this value if you have a sequence of low frame rate images or worms that move unusually fast.

The number of points used to describe the worm median axis. Reducing the number of points increases processing speed. We recommend a minimum value of 31.

When enabled, this option maintains a uniform width profile along the worm body. This option is especially useful when dealing with low resolution images where the worm radius is less than 7 or 8 pixels. Note that this mode does not allow automatic detection of the head and the tail. If width fitting is enabled, WormLab attempts to fine-tune the width profile of the worm while maintaining the uniformity of the main body. If the fitting is successful head and tail can theoretically be distinguished from one another automatically.