An Efficient Deep Learning Model For Single-Cell Segmentation And Tracking In Time-Lapse Microscopy

DeepSea is a deep-learning model designed to automate the segmentation and tracking of single cells in time-lapse microscopy images, specifically tailored for phase-contrast imaging. The DeepSea software excels in capturing cellular dynamics such as cell division, mitosis, and morphology with high precision. It has been particularly effective in analyzing cell size regulation in embryonic stem cells, showcasing its potential for advancing cellular biology research.

DeepSea distinguishes itself from existing solutions by being highly efficient and fast, while maintaining superior accuracy compared to traditional models. The model is specifically tailored for phase-contrast imaging, a common but challenging modality where existing solutions have been less effective. Unlike other models that focus solely on cell segmentation or tracking, DeepSea integrates both functionalities, offering a streamlined and more efficient approach to cellular data analysis. This allows DeepSea to successfully quantify a wide array of cellular biological features, including but not limited to cell division cycles, mitosis, morphology, and cell size, providing a comprehensive solution for researchers in the field.

Key features:

Data Preprocessing: Importing time-lapse microscopy images, particularly phase-contrast images, and pre-processing them to be compatible with the deep-learning model.

Trainable Segmentation: Utilizing a deep-learning algorithm that is capable of being trained on different cell types to perform high-precision segmentation of individual cells in each image frame.

Trainable Temporal Tracking: Sequentially tracking the segmented cells over multiple time frames to observe changes in cellular biological features like cell division, mitosis, and morphology.

Feature Quantification: Extracting and quantifying key cellular features from the segmented and tracked cells, such as cell size, morphological characteristics, and division cycles.

Output and Analysis: Providing comprehensive analytics and visualizations to facilitate further biological interpretation or to serve as input for other computational biology methods.