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Automated Behavioral Analysis Using Instance Segmentation by Yang, Chen; Forest, Jeremy; Einhorn, Matthew; Cleland, Thomas A. is a scholarly article available to read on EtoBox.

What is Automated Behavioral Analysis Using Instance Segmentation about?

Animal behavior analysis plays a crucial role in various fields, such as life science and biomedical research. However, the scarcity of available data and the high cost associated with obtaining a large number of labeled datasets pose significant challenges. In this research, we propose a novel approach that leverages instance segmentation-based transfer learning to address these issues. By capitalizing on fine-tuning the classification head of the instance segmentation network, we enable the tracking of multiple animals and facilitate behavior analysis in laboratory-recorded videos. To demonstrate the effectiveness of our method, we conducted a series of experiments, revealing that our approach achieves exceptional performance levels, comparable to human capabilities, across a diverse range of animal behavior analysis tasks. Moreover, we emphasize the practicality of our solution, as it requires only a small number of labeled images for training. To facilitate the adoption and further development of our method, we have developed an open-source implementation named Annolid (An annotation and instance segmentation-based multiple animal tracking and behavior analysis package). The co

Author

Yang, Chen; Forest, Jeremy; Einhorn, Matthew; Cleland, Thomas A.

Published

2023

Language

EN