Tools for segmentation and tracking in microscopy build on top of Segment Anything. Segment and track objects in microscopy images interactively with a few clicks!
We implement napari applications for:
- interactive 2d segmentation (Left: interactive cell segmentation)
- interactive 3d segmentation (Middle: interactive mitochondria segmentation in EM)
- interactive tracking of 2d image data (Right: interactive cell tracking)
If you run into any problems or have questions regarding our tool please open an issue on Github or reach out via image.sc using the tag micro-sam and tagging @constantinpape.
Please check the documentation for details on how to install and use micro_sam. You can also watch the quickstart video or all video tutorials.
We welcome new contributions!
If you are interested in contributing to micro-sam, please see the contributing guide. The first step is to discuss your idea in a new issue with the current developers.
If you are using this repository in your research please cite
- our preprint
- and the original Segment Anything publication.
- If you use a
vit-tinymodels please also cite Mobile SAM.
There are a few other napari plugins build around Segment Anything:
- https://github.com/MIC-DKFZ/napari-sam (2d and 3d support)
- https://github.com/royerlab/napari-segment-anything (only 2d support)
- https://github.com/hiroalchem/napari-SAM4IS
Compared to these we support more applications (2d, 3d and tracking), and provide finetuning methods and finetuned models for microscopy data. WebKnossos also offers integration of SegmentAnything for interactive segmentation.
New in version 1.0.0
This release mainly fixes issues with the previous release and marks the napari user interface as stable.
New in version 0.5.0
This version includes a lot of new functionality and improvements. The most important changes are:
- Re-implementation of the annotation tools. The tools are now implemented as napari plugin.
- Using our improved functionality for automatic instance segmentation in the annotation tools, including automatic segmentation for 3D data.
- New widgets to use the finetuning and image series annotation functionality from napari.
- Improved finetuned models for light microscopy and electron microscopy data that are available via bioimage.io.
New in version 0.4.1
- Bugfix for the image series annotator. Before the automatic segmentation did not work correctly.
New in version 0.4.0
- Significantly improved model finetuning
- Update the finetuned models for microscopy, see details in the doc
- Training decoder for direct instance segmentation (not available via the GUI yet)
- Refactored model download functionality using pooch
New in version 0.3.0
- Support for ellipse and polygon prompts
- Support for automatic segmentation in 3d
- Training refactoring and speed-up of fine-tuning
New in version 0.2.1 and 0.2.2
- Several bugfixes for the newly introduced functionality in 0.2.0.
New in version 0.2.0
- Functionality for training / finetuning and evaluation of Segment Anything Models
- Full support for our finetuned segment anything models
- Improvements of the automated instance segmentation functionality in the 2d annotator
- And several other small improvements
New in version 0.1.1
- Fine-tuned segment anything models for microscopy (experimental)
- Simplified instance segmentation menu
- Menu for clearing annotations
New in version 0.1.0
- We support tiling in all annotators to enable processing large images.
- Implement new automatic instance segmentation functionality:
- That is faster.
- Enables interactive update of parameters.
- And also works for large images by making use of tiled embeddings.
- Implement the
image_series_annotatorfor processing many images in a row. - Use the data hash in pre-computed embeddings to warn if the input data changes.
- Create a simple GUI to select which annotator to start.
- And made many other small improvements and fixed bugs.
New in version 0.0.2
- We have added support for bounding box prompts, which provide better segmentation results than points in many cases.
- Interactive tracking now uses a better heuristic to propagate masks across time, leading to better automatic tracking results.
- And have fixed several small bugs.