Cell Cycle State Prediction Using Graph Neural Networks

Proceedings - International Conference on Image Processing, vol. 10954, pp. 2916–2922

Abstract

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 semi-automated 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.

Authors 4

1. Sayan Acharya

   Jadavpur University

   Affiliation as printed

   Jadavpur University,Computer Science and Engineering Department,Kolkata,India

2. Aditya Ganguly

   Jadavpur University

   Affiliation as printed

   Jadavpur University,Computer Science and Engineering Department,Kolkata,India

3. Ram Sarkar

   Jadavpur University

   Affiliation as printed

   Jadavpur University,Computer Science and Engineering Department,Kolkata,India

4. Abin Jose Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany