Kallyope
Deciphering the gut-brain axis through AI-driven drug discovery for transformative therapeutics.

The industry-standard deep learning framework for precise, generalist cellular segmentation.
Cellpose is a state-of-the-art, anatomically-aware deep learning framework designed specifically for biological image segmentation. Unlike traditional thresholding methods, Cellpose utilizes a unique vectorized flow representation to distinguish individual cells, even in high-density or low-contrast environments. By 2026, it has solidified its position as the 'gold standard' for zero-shot cellular segmentation, bridging the gap between raw microscopy data and high-throughput quantitative analysis. The architecture is built on a modified U-Net that predicts horizontal and vertical gradients (flows) and cell probabilities, ensuring that segmented objects maintain topological integrity. With the maturation of Cellpose 3.0, the framework now includes advanced image restoration (denoising and deblurring) integrated directly into the segmentation pipeline, significantly reducing the hardware requirements for high-resolution imaging. Its market position is unique: while commercial competitors like Imaris or Arivis offer high-end visualization, Cellpose remains the preferred choice for researchers and developers due to its Python-native integration, Napari-based GUI, and a massive community-driven model zoo that allows for specialized fine-tuning with minimal training data.
Uses a deep neural network to predict horizontal and vertical flow gradients, allowing the algorithm to follow the 'topography' of a cell to its center.
Deciphering the gut-brain axis through AI-driven drug discovery for transformative therapeutics.
Accelerating drug discovery through an end-to-end generative AI pipeline for target identification, molecular design, and clinical trial prediction.
The industry-standard interactive visualization tool for integrated exploration of large-scale genomic datasets.
Unlocking the causal biology of disease through Gemini Digital Twins.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
An interactive GUI workflow where users can correct masks and immediately retrain the model on those corrections.
Integrates image restoration models like 'upsample' or 'denoise' before the segmentation head.
Supports the Omnipose architecture for elongated and bacterial cell types.
Automatically scales the neural network weights based on the median cell diameter in pixels.
Can simultaneously utilize nuclear and cytoplasmic channels to define cell boundaries.
Native PyTorch implementation leveraging NVIDIA GPUs for real-time processing.
Manually counting thousands of cells across 384-well plates is impossible and error-prone.
Registry Updated:2/7/2026
Fluctuating brightness and cell movement make tracking difficult over time.
Standard models fail on non-spherical, elongated bacterial shapes.