Knowledge Base

Test Apparatus and Background

Test Apparatus

EthoVision XT's neural networks have been trained with videos of:

• One subject per arena, subjects of uniform color: Open field (regular or with round objects), PhenoTyper (with or without bedding material), Elevated Plus Maze, Three-Chamber Social Approach Cage, Barnes Maze, Fear Conditioning Cage with floor grid, Y-maze (with no objects), and a maze with multiple chambers and openings.
• One subject per arena, hooded rats: Open field (regular or with round, rectangular, or triangular objects), PhenoTyper (with or without bedding material), and Elevated Plus Maze.
• Two subjects per arena: Open field (with no objects), PhenoTyper (with or without bedding material), and home cage.

Size of the Arena

• One subject per arena: No particular requirements, but see the Video Image section above.
• Two subjects per arena: Small containers reduce the probability that the two animals are separated for sufficient time, which is a necessary condition for success with Deep Learning-based individual recognition.

Background

• If the animal is small relative to the arena and there is no way to zoom in the camera image, try to improve the contrast with the background, for example by providing more infrared light, in order to compensate for the lower level of spatial detail.
• If contrast is too low, increase lighting, open up the lens aperture, or increase the camera gain.
• Floor grids are compatible with the Deep Learning detection technique. If necessary, reduce the amount of light to minimize the reflections and shadows caused by the metal bars.
• Bedding material can also be used with Deep Learning-based tracking. However, there should be enough contrast with the animals. If a dark individual is frequently going undetected, try increasing the lighting or, if possible, switch to another type of bedding.
• Too much bedding or nest material can cause occlusions when the animal digs in it, in which case subject detection and discrimination may not work properly.
• For one-subject tracking, the apparatus can contain objects, such as in the Novel Object Recognition test or the Sociability test. Whenever possible, use objects of a color different from the subject's color.
• When working with multiple arenas simultaneously, check that there are no blind corners, as these may reduce the detection rate.

Corridors and Walls

For elevated plus mazes, radial mazes and other apparatuses with corridors, make sure that the walls are not of the same color as the subject.

In the following example, an excess of light from one side of the test room makes the top of the walls of this plus maze look white. A white mouse is still detected but when it touches the walls the nose-point and/or the tail-base point are no longer detected. Dim the lights and adjust their orientation to reduce the reflections.

When you define the Cutout size in the Detection Settings, try not to include walls and objects in the cutout box, especially if those objects are the same color as the animal. See Adjust the Settings for Nose-Tail Base Detection (Deep Learning).

Hole Boards

Deep learning-based tracking also performs well in situations of low contrast between the subject and the holes.

TIP: Define the target hole as a hidden zone. See Shelters and Other Hidden Zones.

Apparatuses with Backlight

• One subject per arena: Backlight is compatible but may not be ideal. In one example, the mouse explores a plus maze with an infrared-lit background. When the mouse dips the head below the level of the open arms, the nose is not found. To solve this, place dim lights on the floor.
• Two subjects per arena: Backlight is not compatible.

Tethered Animals

• One subject per arena: When running experiments with tethered animals, the contour of the subjects seen by EthoVision XT is often disrupted by the fiber. The Deep Learning technique may not be able to resolve the position of the nose. Deep Learning has not been tested with tethered animals. Therefore, run a few tests with sample videos to make sure that detection of the nose point is accurate enough. To improve detection of tethered subjects, use the Dilation and Erosion filter options. See an example in Advanced Detection Settings: Subject Contour.
• Two subjects per arena: This setup has not been tested thoroughly; therefore applying Deep Learning is not recommended.

Recording Protocol

• One subject per arena: No particular limitations.
• Two subjects per arena: Preferably, tracking should start when both animals are in the arena. The software also works when you release one animal first, then the other. However, for best results, ensure that the second animal is released within up to 30 seconds after the first.
• With the Trial Control Settings, you can ensure that the data acquisition starts when both subjects are in the arena. See Start the Trial in the Social Interaction Test.

Behavior Recognition

It is not yet possible to combine Deep Learning for body point detection and Behavior Recognition for behavior classification in the same experiment. When you select Deep Learning under Body Point Detection Technique in the Experiment Settings, the Behavior Recognition option is grayed out.

Test Results

Deep Learning was tested with various combinations of PCs, graphics cards and cameras. A high-end workstation is recommended when working with Deep Learning-based tracking. Click the link that applies based on which camera type you have:

• GigE cameras
• USB 3.0 cameras
• Analog cameras

In all tests, live tracking and save video were used, with one arena per camera. All tests were based on an open-field experiment with a black mouse and a clearly contrasting gray background.

See Also

• Cameras Supported by EthoVision XT
• System Requirements > Hardware