{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Cellpose segments cells and nuclei in microscopy images using deep learning, with support for 2D/3D data, custom model training, and image restoration.","skillfed_tags":["microscopy-image-analysis","cell-segmentation","deep-learning"],"use_cases":["Segment cell nuclei and cytoplasm in fluorescence microscopy images for quantitative analysis.","Process 3D volumetric data from confocal or light-sheet microscopy with orthogonal refinement.","Fine-tune a pre-trained model on your own labeled dataset to improve accuracy for specific cell types or imaging modalities.","Restore degraded microscopy images before segmentation to improve mask quality.","Batch-process large image collections via the Hugging Face web interface without local GPU setup."],"what_it_does":"Cellpose is a deep-learning-based segmentation tool designed for identifying cells and nuclei in microscopy images. It combines a generalist neural network backbone with support for custom model training, allowing users to adapt it to their own data without extensive ML expertise. The package handles challenging real-world imaging conditions\u2014shot noise, blur, undersampling, contrast inversions, variable channel order, and mixed object sizes\u2014and works in both 2D and 3D.\n\nThe latest version integrates segment_anything for improved generalization and includes image restoration capabilities to clean up noisy or degraded input before segmentation. You can use pre-trained models out of the box, fine-tune them on labeled data via human-in-the-loop training, or run batch processing through the web interface. The package ships with a GUI, command-line tools, and a Python API, making it accessible for both interactive exploration and automated pipelines.","worth_installing":"Yes, if you work with microscopy image segmentation. Cellpose is actively maintained, well-documented, and handles real-world imaging challenges. Install friction is low for standard ML environments. Note that pre-trained models are CC-BY-NC licensed; if commercial use of those models is required, verify licensing implications. No known security vulnerabilities."},"id":"cellpose","links":{"html":"https://skillfed.io/packages/cellpose","md":"https://skillfed.io/packages/cellpose.md","pypi":"https://pypi.org/project/cellpose/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-14","license_spdx":null,"license_treatment":"permissive","name":"cellpose","python_support":"unspecified","summary":"anatomical segmentation algorithm"},"popularity":{"monthly_downloads":115659,"position":12241,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"4.2.1.1"}