Weakly-supervised Temporal Segmentation of Cell-cycle Stages with Center-cell Focus using Recurrent Neural Networks

Jose, Abin; Roy, Rijo; Stegmaier, Johannes

doi:10.1007/978-3-658-41657-7_47

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2024

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Cited by 2 publications

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Cell Cycle State Prediction Using Graph Neural Networks

Acharya,

Ganguly,

Sarkar

et al. 2024

Preprint

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Mitosis is a crucial process ensuring the faithful transmission of the genetic information stored in the cell nucleus. Aberrations in this intricate process pose a significant threat to an organism’s health, leading to conditions like cancer and various diseases. Hence, the study of mitosis holds paramount importance. Recent investigations have involved manual and semiautomated analyses of time-lapse microscopy images to understand mitosis better. This paper introduces an approach for predicting mitosis stages, employing a Convolutional Neural Network (CNN) as the initial feature extractor, followed by a Graph Neural Network (GNN) for predicting cell cycle states. A distinctive timestamp is incorporated into the feature vectors, treating this information as a graph to leverage internal interactions for predicting the subsequent cell state. To assess performance, experiments were conducted on three datasets, demonstrating that our method exhibits comparable efficacy to state-of-the-art techniques.

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