An unsupervised long short-term memory neural network for event detection in cell videos

TL;DR

This paper introduces an unsupervised LSTM-based neural network for detecting and classifying cellular events in cell videos, eliminating the need for manual annotation and demonstrating competitive accuracy on challenging microscopy datasets.

Contribution

The authors develop a novel unsupervised branched LSTM network that effectively detects cellular events without manual labels, outperforming some supervised methods on complex datasets.

Findings

Unsupervised LSTM achieves comparable or better F1-score than supervised methods.

The model generalizes across different cell video conditions.

The approach reduces reliance on manual annotation.

Abstract

We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events in cell video sequences. Cells in images that are captured from various biomedical applications usually have different shapes and motility, which pose difficulties for the automated event detection in cell videos. Current methods to detect cellular events are based on supervised machine learning and rely on tedious manual annotation from investigators with specific expertise. So that our LSTM network could be trained in an unsupervised manner, we designed it with a branched structure where one branch learns the frequent, regular appearance and movements of objects and the second learns the stochastic events, which occur rarely and without warning in a cell video sequence. We tested our network on a publicly available dataset of densely packed stem cell phase-contrast microscopy images undergoing cell division. This dataset is considered to be more challenging that a dataset with sparse cells. We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method. We used an F1-score, which is a balanced measure for both precision and recall. Our results show that our unsupervised method has a higher or similar F1-score when compared to two fully supervised methods that are based on Hidden Conditional Random Fields (HCRF), and has comparable accuracy with the current best supervised HCRF-based method. Our method was generalizable as after being trained on one video it could be applied to videos where the cells were in different conditions. The accuracy of our unsupervised method approached that of its supervised counterpart.