Advancing single-cell analysis: deep learning for cell segmentation and tracking with improved metrics
OA Version
Citation
Abstract
Advances in microscopy software and hardware have dramatically accelerated the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. To address this challenge, we leverage deep learning to develop robust software for cell segmentation and tracking. First, we present DeLTA 2.0, a convolutional neural network-based pipeline optimized for high throughput analysis of single-cell images on two-dimensional surfaces, enabling quantification of gene expression and cell growth. Next, we introduce Cell-TRACTR, a transformer-based model that integrates segmentation and tracking into a unified framework, delivering enhanced tracking accuracy. Lastly, we propose Cell-HOTA, a novel cell tracking metric that balances detection, association and division accuracy, offering a balanced and interpretable evaluation of cell tracking performance. Collectively, these contributions advance the field of cell tracking, enabling more accurate and robust single-cell data analysis.
Description
2025
License
Attribution 4.0 International