Journal article Open Access

Robust mouse tracking in complex environments using neural networks

Brian Q. Geuther; Sean P. Deats; Kai J. Fox; Steve A. Murray; Robert E. Braun; Jacqueline K. White; Elissa J. Chesler; Cathleen M. Lutz; Vivek Kumar

Both the training data and trained models used in the paper are found here.

Dataset description

Information for each dataset falls into 3 folders. Filenames portray the dataset split used in the paper that they belong to (eg Training_1.png or Validation_1.png).

  • Ref/*.png: Input image (image before annotation)
  • Seg/*.png: Segmentation image. Values = 0 are background. Values > 0 are mouse.
  • Ell/*.txt: Ellipse-fit data. Data is tab-delimited as follows:
    • X Center of Ellipse (px)
    • Y Center of Ellipse (px)
    • Minor Axis Length of Ellipse (px)
    • Major Axis Length of Ellipse (px)
    • Angle Direction (Degrees). 0 is down with + values going counter-clockwise.

Trained Model Description

We also release models trained on all the subsets of training data we share. Each trained model was trained using our code over on Github:

Brief descriptions of the training subsets

Please read the associated paper for additional detail. A brief summary of the environment is added here:

Standard Open Field Strain Survey

We annotated 16234 training and 568 validation images of a single mouse in the same open field. The mouse can be one of multiple coat colors, but visually appears as a black, light-grey, or white color. In the case the mouse’s posture created a poor ellipse-fit, portions of the mouse were removed (such as tail) to enable a good ellipse-fit.

24Hr Open Field Dataset

We annotated 2099 training and 93 validation images of a single mouse in the same open field listed above augmented with bedding and a food container. All mice in this experiment appear black on video. There are 2 states, with visible light and with only infrared. The infrared-only imaging contains much higher visual noise.

KOMP Open Field Dataset

We annotated 1000 training and 83 validation images of a single mouse in JAX’s KOMP2 open field arena. All mice have a black coat color.

Test Ground Truth Dataset

To test the robustness of our system against conventional trackers that build a background model from multiple frames in a video, we re-sampled video a 20 minute video at 1 frame per second and annotated all the resulting frames (1179-1200 frames). We did this for the 6 environments in the paper of varying difficulty (Black, Gray, Piebald, Albino, 24Hr, KOMP2). The format of this data follows a DataSubset_FrameNumber format instead of Training/Validation_FrameNumber format.

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  • Geuther, Brian Q., et al. "Robust mouse tracking in complex environments using neural networks." Communications biology 2.1 (2019): 1-11.

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