janelia-cellmap/dacapo

A framework for easy application of established machine learning techniques on large, multi-dimensional images.

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Emerging

This tool helps life scientists and researchers efficiently process extremely large, multi-dimensional biological images, such as those from electron microscopy, to automatically identify and segment structures like cells or organelles. You provide your raw image data and define what you want to find, and it outputs precise segmentations that highlight these structures. It's designed for biologists, microscopists, and anyone working with high-resolution image analysis in scientific research.

No commits in the last 6 months.

Use this if you need to apply established machine learning methods to automatically segment features within very large, complex 3D or 4D biological image datasets.

Not ideal if you are working with standard 2D images, small datasets, or do not require advanced machine learning for image segmentation.

bioimaging microscopy cell-biology image-segmentation life-sciences
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 15 / 25

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60

Forks

10

Language

Python

License

BSD-3-Clause

Last pushed

Sep 15, 2025

Commits (30d)

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