
Deep learning-powered single-cell analysis for biological imaging and spatial biology.
DeepCell is a state-of-the-art software library and cloud-native ecosystem designed for single-cell analysis using deep learning. Developed primarily by the Van Valen Lab at Caltech, the architecture leverages TensorFlow and Keras to solve complex biological image processing tasks, such as cell segmentation and lineage tracking. Its flagship model, Mesmer, is a pre-trained deep learning model capable of high-accuracy nuclear and whole-cell segmentation across diverse tissue types and imaging modalities (e.g., fluorescence, brightfield, MIBI-TOF, CODEX). As of 2026, DeepCell has evolved into a critical infrastructure for spatial biology, providing the DeepCell Kiosk—a Kubernetes-based system that allows for massive horizontal scaling of inference tasks. This enables researchers to process terabyte-scale microscopy datasets in hours rather than weeks. The platform's 2026 market position is defined by its ability to bridge the gap between raw microscopy data and quantitative biological insights, offering researchers a reproducible, open-source pipeline that competes directly with proprietary high-content screening software by providing superior generalization across varying biological morphologies.
A deep learning model trained on 'TissueNet', a massive dataset of human tissue images, optimized for segmenting cell boundaries and nuclei simultaneously.
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A Kubernetes-native application that manages a pool of workers to process large-scale imaging experiments in parallel.
Utilizes AI to predict fluorescent-like labels from brightfield images, reducing the need for phototoxic staining.
Advanced support for MIBI-TOF and CODEX datasets where dozens of channels are used to identify cell phenotypes.
Integrated deep learning framework for linking cell identities across frames in time-lapse microscopy.
Optimized data pipeline that converts raw images into TFRecords for high-speed training and inference.
Workflow integration for human-in-the-loop annotation where the model identifies uncertain areas for manual correction.
Identifying spatial relationships between immune cells and cancer cells in highly multiplexed tissue sections.
Registry Updated:2/7/2026
Tracking cell growth and division over 48 hours without excessive phototoxicity.
Quantifying morphological changes in millions of cells across 384-well plates.