Automatic Classification of Normal and Abnormal Cell Division Using Deep Learning Analysis of Mitosis Videos

Abstract

In recent years, there has been a surge in the development of methods for cell segmentation and tracking, with initiatives such as the Cell Tracking Challenge driving progress in the field. Most studies focus on regular cell population videos in which cells are segmented, cell tracks followed, and parental relationships annotated. However, DNA damage induced by genotoxic drugs or ionizing radiation provide additional abnormal cellular events of interest since they lead to aberrant behaviors such as abnormal cell divisions (i.e., resulting in a number of daughter cells different from two) and cell death. The dynamic development of those abnormal events can be followed using time lapse microscopy to be further analyzed. With this in mind, we developed an automatic mitosis classifier that categorizes small mitosis image sequences centered around a single cell as "Normal" or "Abnormal." These mitosis sequences were extracted from videos of cell populations exposed to varying levels of radiation that affect the cell cycles development. Such an approach can aid in detecting, tracking, and characterizing the behavior of the entire population. In this study, we explored several deep-learning architectures for working with 12-frame mitosis sequences. We found that a network with a ResNet50 backbone, modified to operate independently on each video frame and then combined using a Long Short-Term Memory (LSTM) layer, produced the best results in the classification (mean F1-score: 0.93 {+/-} 0.06). In future work, we plan to integrate the mitosis classifier in a cell segmentation and tracking pipeline to build phylogenetic trees of the entire cell population after genomic stress. Author SummaryIn recent years, there has been a growing interest in developing methods to analyze videos of cell populations, which show how cells move and divide over time. Typically, researchers focus on developing methods to automatically identify and track individual cells and their divisions. However, exposure to anticancer drugs or radiation can cause uncommon behaviors, such as abnormal cell divisions, which are of interest to experts studying the effects of these agents on cell behavior. To address this issue, we developed an automated tool that can determine whether a specific cell division seen in a video is normal or abnormal. We used video microscopy to capture small sequences of cell division, and then trained a deep-learning model to classify these sequences as either normal or abnormal. We found that our model achieved a high level of accuracy in this task. Our tool has the potential to aid experts in identifying abnormal cellular events, providing insights into the effects of genotoxic agents on cell behavior. In future work, we plan to integrate our tool into more complex methods for analyzing cell population videos, which may help us better understand the impact of toxic agents on the behavior of the entire cell population.