CVPR 2023 - Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses

In this episode we discuss Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses by Junbong Jang, Kwonmoo Lee, Tae-Kyun Kim. The paper proposes a deep learning-based method for tracking the dynamic changes of cellular morphology in live cell videos. The proposed method includes point correspondence and considering local shapes and textures on the contour, which previous methods did not. Unsupervised learning is used, consisting of mechanical and cyclical consistency losses, to train the contour tracker. The proposed method outperforms existing methods and is publicly available.