Machine-Learning Based Automatic and Real-time Detection of Mouse Scratching Behaviors

Abstract

Scratching is a main behavioral response accompanied by acute and chronic itch conditions, and has been quantified as an objective correlate to assess itch in studies using laboratory animals. Scratching has been counted mostly by human annotators, which is a time-consuming and laborious process. It has been attempted to develop automated scoring methods using various strategies, but they often require specialized equipment, costly software, or implantation of device which may disturb animal behaviors. To complement limitations of those methods, we have adapted machine learning-based strategy to develop a novel automated and real-time method detecting mouse scratching from experimental movies captured using monochrome cameras such as a webcam. Scratching is identified by characteristic changes in pixels, body position, and body size by frame as well as the size of body. To build a training model, a novel two-step J48 decision tree-inducing algorithm along with a C4.5 post-pruning algorithm was applied to three 30-min video recordings in which a mouse exhibits scratching following an intradermal injection of a pruritogen, and the resultant frames were then used for the next round of training. The trained method exhibited, on average, a sensitivity and specificity of 95.19% and 92.96%, respectively, in a performance test with five new recordings. This result suggests that it can be used as a non-invasive, automated and objective tool to measure mouse scratching from video recordings captured in general experimental settings, permitting rapid and accurate analysis of scratching for preclinical studies and high throughput drug screening.

Keywords: Decision tree; Itch; Machine learning; Mouse; Pruritus; Scratching.

Fig. 1. (a) Overall flowchart (a) and sample image (b) showing the procedures to develop a novel automatic method detecting scratching. Experimental movies recoded following an intradermal injection of an itch-causing compound into the nape of the neck of a mouse were registered and binarized for the frame-to-frame analysis. Following a process of noise reduction, four characteristic changes accompanying scratching, changes in body size as well as changes in pixels, body size and body position by frame, were extracted from the movies. These features were utilized to determine scratching from other homecage behaviors by a two-step decision tree-inducing algorithm during two rounds of training procedures to build a novel detection method.

Fig. 2. Normalized histograms and distributions and ANOVA analysis results for scratching (Scr, red bars and lines) and no scratching (NoScr, blue bars and lines) behaviors in (a) frame-to-frame pixel difference, (b) frame-to-frame body position difference, (c) frame-to-frame body size difference and (d) body size. (^**p-value<0.0001).

Fig. 3. Decision tree of the fine modulated model obtained in the second step, where features, decisions, and outcomes are expressed in shapes of ellipse, line, and square, respectively. Features are depicted in colored ellipses with letters enclosed, where ‘fd’, ‘pd’, ‘sd’ or ‘sz’ refer the frame-to-frame pixel difference, the frame-to-frame body position difference, the frame-to-frame body size difference or the body size, respectively. Decisions are displayed in different patterns of lines, in which solid or dotted lines are cases for the corresponding feature not to be or to be greater than the number specified beside the feature, respectively. Outcomes are displayed as a letter ‘N’ for non-scratching or ‘S’ for scratching on a colored square with white or grey, respectively.