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Cellpose — generalist deep learning algorithm for cellular and nuclear segmentation in microscopy images. Provides GPU-accelerated 2D and 3D instance segmentation using gradient flow representations,
Use with AI
Install the MCP server or CLI to instantly fetch Cellpose documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/cellpose
Use when working with MONAI — the Medical Open Network for AI — a PyTorch-based framework for deep learning in medical imaging. Covers 3D image segmentation, classification, detection, and reconstruct
3 shared topics • 1 shared operation
DeepCell — deep learning library for single-cell analysis of biological images using TensorFlow. Provides pretrained models for cell segmentation including Mesmer (multiplexed tissue imaging), nuclear
2 shared topics • 1 shared operation
QuPath — open-source platform for whole slide image analysis and digital pathology. Provides interactive tools for tissue detection via thresholding, cell detection and positive cell classification (H
2 shared topics • 1 shared operation
STARmap (Spatially-resolved Transcript Amplicon Readout mapping) — in situ spatial transcriptomics method that combines hydrogel-tissue chemistry with sequencing-by-hybridization (SEDAL) for multiplex
2 shared topics • 1 shared operation
ZeroCostDL4Mic is a Google Colab-based platform providing zero-cost deep learning for fluorescence microscopy image analysis. Supports segmentation (U-Net, StarDist, Cellpose), denoising (Noise2Void,
2 shared topics • 1 shared operation