Analysis of cell behavior in videos of fluorescence imagery using uncertainty-based Bayesian transformer

Kyungsu Lee; Moon Hwan Lee; Jae Youn Hwang

doi:10.1117/12.3002073

13 March 2024 Analysis of cell behavior in videos of fluorescence imagery using uncertainty-based Bayesian transformer

Kyungsu Lee, Moon Hwan Lee, Jae Youn Hwang

Proceedings Volume PC12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII; PC128460C (2024) https://doi.org/10.1117/12.3002073
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

The CNNs have significantly advanced in analyzing cellular movements. Unfortunately, the CNN-based networks incorporate the information loss caused by the intrinsic characteristics of the convolution operators, leading to degrading the performance of cell segmentation and tracking. Researchers have proposed consecutive CNNs to overcome these limitations, although these models are still in the preproduction stage. In this study, we present a novel approach that utilizes cumulative CNNs to segment and track cells in fluorescence videos. Our method incorporates the state-of-the-art Vision Transformer (ViT) and Bayesian Network to improve accuracy and performance. By leveraging the ViT architecture and Bayesian network, we aim to mitigate information losses and enhance the precision of cell segmentation and tracking tasks.

Conference Presentation

Citation Download Citation

Kyungsu Lee, Moon Hwan Lee, and Jae Youn Hwang "Analysis of cell behavior in videos of fluorescence imagery using uncertainty-based Bayesian transformer", Proc. SPIE PC12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII, PC128460C (13 March 2024); https://doi.org/10.1117/12.3002073

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