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

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Training deep-learning models for biomedical images has always been a problem due to the lack of annotated data. Here we propose using a model and a training approach for the weakly-supervised temporal classification of cell-cycle stages during mitosis. Instead of using annotated data, by using an ordered set of classes called transcript, our proposed approach classifies the cell-cycle stages of cell video sequences. The network design helps to propagate information in time using Recurrent Neural Network and helps to focus the features on the center-cell. The algorithm is evaluated on four datasets from LiveCellMiner and has a performance close to the supervised approaches, which is impressive, considering that annotated data is not used in training.

Authors 3

1. Abin Jose corresponding Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University

2. Rijo Roy Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University

3. Johannes Stegmaier Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University