Semi-supervised estimation of event temporal length for cell event detection

TL;DR

This paper introduces a semi-supervised approach to determine the optimal input sequence length for LSTM-based mitosis detection in cell videos, reducing annotation effort while maintaining high accuracy.

Contribution

It proposes an unsupervised method to estimate cell event durations and infer the optimal sequence length, improving detection performance with fewer annotated frames.

Findings

Optimal sequence length reduces performance degradation.

High F1-scores achieved with only 18 annotated frames.

Performance surpasses methods using full annotation.

Abstract

Cell event detection in cell videos is essential for monitoring of cellular behavior over extended time periods. Deep learning methods have shown great success in the detection of cell events for their ability to capture more discriminative features of cellular processes compared to traditional methods. In particular, convolutional long short-term memory (LSTM) models, which exploits the changes in cell events observable in video sequences, is the state-of-the-art for mitosis detection in cell videos. However, their limitations are the determination of the input sequence length, which is often performed empirically, and the need for a large annotated training dataset which is expensive to prepare. We propose a novel semi-supervised method of optimal length detection for mitosis detection with two key contributions: (i) an unsupervised step for learning the spatial and temporal locations of cells in their normal stage and approximating the distribution of temporal lengths of cell events and, (ii) a step of inferring, from that distribution, an optimal input sequence length and a minimal number of annotated frames for training a LSTM model for each particular video. We evaluated our method in detecting mitosis in densely packed stem cells in a phase-contrast microscopy videos. Our experimental data prove that increasing the input sequence length of LSTM can lead to a decrease in performance. Our results also show that by approximating the optimal input sequence length of the tested video, a model trained with only 18 annotated frames achieved F1-scores of 0.880-0.907, which are 10% higher than those of other published methods with a full set of 110 training annotated frames.