Automated Video-Based Rodent Behavior Analysis

Le, Van Anh

Automated Video-Based Rodent Behavior Analysis

dc.contributor.advisor	Murari, Kartikeya
dc.contributor.author	Le, Van Anh
dc.contributor.committeemember	Forkert, Nils Daniel
dc.contributor.committeemember	Yanushkevich, Svetlana
dc.contributor.committeemember	Bento, Mariana Pinheiro
dc.contributor.committeemember	Ravichandran, Avinash
dc.date	2024-05
dc.date.accessioned	2024-02-06T19:42:48Z
dc.date.available	2024-02-06T19:42:48Z
dc.date.issued	2024-01-30
dc.description.abstract	Rodents 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 methods
dc.identifier.citation	Le, V. A. (2024). Automated video-based rodent behavior analysis (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/118165
dc.identifier.uri	https://doi.org/10.11575/PRISM/43009
dc.language.iso	en
dc.publisher.faculty	Schulich School of Engineering
dc.publisher.institution	University of Calgary
dc.rights	University 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.
dc.subject	Rodent behavior recognition
dc.subject	Animal tracking
dc.subject	Neural network
dc.subject	Classification
dc.subject	Feature extraction
dc.subject	Neural network
dc.subject	Dataset
dc.subject	Segmentation
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Engineering
dc.title	Automated Video-Based Rodent Behavior Analysis
dc.type	doctoral thesis
thesis.degree.discipline	Engineering – Electrical & Computer
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I require a thesis withhold – I need to delay the release of my thesis due to a patent application, and other reasons outlined in the link above. I have/will need to submit a thesis withhold application.