Towards Automatic Detection and Quantification of Gait in Arthritic Rats

Rat’s gait analysis plays an important role in the assessment of the impact of certain drugs on the treatment against arthritis, a common misunderstood disease that has a significant incidence worldwide, resulting in a considerable economic and social impact. This disorder damages the joints, structures responsible for the movement of the body, causing pain, stiffness and functional impairment, which have a tremendous impact on the patients’ routines. Currently, the search for new treatments for arthritis relies on the use of animal models (mainly rats and mice) to understand the pathophysiological mechanisms underlying this disorder and to test the therapeutic efficacy of targeted drugs. For that purpose, arthritis is induced in the animal, usually in the knee joint, and the symptoms and behavioral signs displayed by the animal throughout the evolution of the disease and following drug administration are quantified along time. Among the different behavioral signs, the animals’ gait pattern is analyzed to understand, through a continuous monitoring, the disease impairment as well as the therapeutic efficacy of target drugs. This gait monitoring can be done through video analysis, by a manual or automated procedure. Although there are some systems capable of detecting and quantifying the gait changes in rodents automatically, they still present many limitations and researchers often opt for the development of a customized system for the acquisition of the videos, analyzing them manually. Nonetheless, this manual observation and quantification of the gait changes is a laborious, timeconsuming and intensive task that is prone to high user variability, which mainly reveals the need for robust automated gait analysis systems in laboratory animals. Therefore, and bearing in mind the recent advances in machine learning and computer vision fields, as well as their presence in many processing, tracking and recognition applications, this work is driven by the demand to find a solution to make the detection and quantification of the animals’ gait changes an automatic task. Thus, we propose a methodology capable of detecting and segmenting the animal’s body, detecting, segmenting and classifying its paws, and of selecting some frames of interest and extracting some gait metrics from them. This tracking-by-detection framework makes the detection and quantification of the animal gait changes task easier, faster, simpler, more robust and less susceptible to user variability. As far as performance is concerned, the adopted methodology proved to be effective, being able to return frames of interest in 87% of the dataset videos and to return final gait metrics similar to the ones obtained by the experts with a global mean difference of 3,44% ± 4,25. In order to make the proposed framework a free service available to researchers, this was integrated in a web application tool with an user friendly interface. Results of this work also include the creation of a database with 15 videos of the rat’s gait and the manual annotation of 130 frames, as well as an article published in the ninth edition of the Iberian Conference on Pattern Recognition and Image Analysis with the title "Towards Automatic Rat’s Gait Analysis Under Suboptimal Illumination Conditions". The data used for the development of this dissertation was provided by the Faculdade de Medicina da Universidade do Porto.