High-Throughput Low-Cost Segmentation of Brightfield Microscopy Live Cell Images
q-bio.QM, cs.AI, cs.CV, eess.IV
Submitted: 2025-08-17
Updated: 2025-08-23
Journal ref: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol. 14, No. 1, 2723013 (2026)
DOI: 10.1080/21681163.2026.2723013
Project page: https://sartorius-research.github.io/LIVECell
License: http://creativecommons.org/licenses/by/4.0/
The gist: Live cell culture is crucial in biomedical studies for analyzing cell properties and dynamics in vitro.
Terminology
Abstract
Live cell culture is crucial in biomedical studies for analyzing cell properties and dynamics in vitro. This study focuses on segmenting unstained live cells imaged with bright-field microscopy. While many segmentation approaches exist for microscopic images, none consistently address the challenges of bright-field live-cell imaging with high throughput, where temporal phenotype changes, low contrast, noise, and motion-induced blur from cellular movement remain major obstacles. We developed a low-cost CNN-based pipeline incorporating comparative analysis of frozen encoders within a unified U-Net architecture enhanced with attention mechanisms, instance-aware systems, adaptive loss functions, hard instance retraining, dynamic learning rates, progressive mechanisms to mitigate overfitting, and an ensemble technique. The model was validated on a public dataset featuring diverse live cell variants, showing consistent competitiveness with state-of-the-art methods, achieving 93% test accuracy and an average F1-score of 89% (std. 0.07) on low-contrast, noisy, and blurry images. Notably, the model was trained primarily on bright-field images with limited exposure to phase- contrast microscopy (<20%), yet it generalized effectively to the phase-contrast LIVECell dataset, demonstrating modality, robustness and strong performance. This highlights its potential for real- world laboratory deployment across imaging conditions. The model requires minimal compute power and is adaptable using basic deep learning setups such as Google Colab, making it practical for training on other cell variants. Our pipeline outperforms existing methods in robustness and precision for bright-field microscopy segmentation. The code and dataset are available for reproducibility 1.
Sources
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Attention U-Net: Learning Where to Look for the Pancreas
- Mixed Precision Training
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