Murari, KartikeyaLe, Van Anh2024-02-062024-02-062024-01-30Le, V. A. (2024). Automated video-based rodent behavior analysis (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.https://hdl.handle.net/1880/118165https://doi.org/10.11575/PRISM/43009Rodents represent more than 95% of the laboratory animals used in preclinical and neuroscience research. Mouse behavior analysis is an important step to evaluate disease states and normal brain processes. This thesis focuses on developing automatic video-based mouse behavior analysis tools, which allow high throughput assessments and alleviate the limitations of manual analysis. Particularly, we investigated multiple machine-learning based approaches to fill the gaps of existing studies regarding rodent behavior measurements and create reliable computer-assisted frameworks. Firstly, we introduced MaSoMoTr which is a markerless mice tracking tool for social experiments. The tracking workflow incorporated deep-learning-based techniques with conventional handcrafted tracking methods to simultaneously track two mice of the same appearance in controlled settings. The proposed method achieved significant improvement compared to the state-of-the-art pose-estimation-based tracking frameworks. Following that, we developed a social behavior recognition system integrating our tracking tool to identify a set of mouse behaviors in continuous videos recording two interacting mice. Datasets collected and annotated during these two studies have been made publicly available for further research and development. Finally, two approaches were proposed for automatically recognizing single mouse behaviors in two different settings. We investigated the possibility of extracting spatio-temporal features from single mouse recordings using a deep learning structure which combined a 3D convolutional network and a recurrent neural network with Long Short-Term Memory cells. These extracted features were tested to recognize 8 single mouse behaviors in videos belonging to the largest public single mouse dataset and attained promising performance. Next, we proposed a noninvasive video-based method for mouse sleep assessment. The results obtained were highly correlated with commonly used invasive methodsenUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Rodent behavior recognitionAnimal trackingNeural networkClassificationFeature extractionNeural networkDatasetSegmentationArtificial IntelligenceEngineeringdoctoral thesis