DeepCell
A deep learning library for single-cell analysis of biological images, specializing in cell segmentation and tracking.
What is DeepCell?
DeepCell-tf is a deep learning library specifically designed for single-cell analysis of biological images. It enables researchers to segment and track individual cells in 2D, 3D, and time-lapse imaging data using pre-trained models or custom architectures. The library solves the problem of automating quantitative cellular analysis, which is critical for high-throughput biological experiments.
Biologists, bioimage analysts, and computational researchers working with cellular imaging data who need to automate cell segmentation, tracking, and feature extraction.
Developers choose DeepCell-tf because it provides specialized, production-ready models for biological images within a scalable ecosystem. Its integration with TensorFlow 2 allows for flexible model development, while the accompanying DeepCell Toolbox and Kiosk streamline preprocessing and large-scale deployment.
Overview
Deep Learning Library for Single Cell Analysis
Use Cases
Best For
- Segmenting nuclei in fluorescent microscopy images
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