Counting Cells in Time-Lapse Microscopy using Deep Neural Networks

TL;DR

This paper introduces a deep learning model combining ConvNets and LSTM networks for dynamic cell counting in time-lapse microscopy, leveraging temporal information to improve accuracy over static methods.

Contribution

The paper presents a novel spatiotemporal neural network approach for cell counting that utilizes multiple frames, outperforming previous static methods in accuracy.

Findings

Outperforms static cell counting methods on a stem cell dataset.

Utilizes ConvNets and LSTM to incorporate temporal information.

Applicable to counting objects in videos beyond cell microscopy.

Abstract

An automatic approach to counting any kind of cells could alleviate work of the experts and boost the research in fields such as regenerative medicine. In this paper, a method for microscopy cell counting using multiple frames (hence temporal information) is proposed. Unlike previous approaches where the cell counting is done independently in each frame (static cell counting), in this work the cell counting prediction is done using multiple frames (dynamic cell counting). A spatiotemporal model using ConvNets and long short term memory (LSTM) recurrent neural networks is proposed to overcome temporal variations. The model outperforms static cell counting in a publicly available dataset of stem cells. The advantages, working conditions and limitations of the ConvNet-LSTM method are discussed. Although our method is tested in cell counting, it can be extrapolated to quantify in video (or correlated image series) any kind of objects or volumes.