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| # Release Overview | ||
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| **New in version 1.2.2** | ||
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| Fixing minor issues with 1.2.1 for making automatic segmentation CLI more flexible. | ||
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| **New in version 1.2.1** | ||
| This version introduces several changes that are part of three of our recent publications that are built on top of micro_sam: | ||
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| - [medico-sam](https://github.com/computational-cell-analytics/medico-sam), which improves SAM for medical images | ||
| - [peft-sam](https://github.com/computational-cell-analytics/peft-sam), which investigates parameter efficient finetuning for SAM | ||
| - [patho-sam](https://github.com/computational-cell-analytics/patho-sam), which improves SAM for histopathology | ||
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| **New in version 1.2.0** | ||
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| The main changes in this version are: | ||
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| - Installation using only conda-forge dependencies and simplified installation instructions (on Linux and Mac OS). | ||
| - Fix annotation in napari widgets with scale factors. | ||
| - Support for several parameter-efficient training methods. | ||
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| **New in version 1.1.1** | ||
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| Fixing minor issues with 1.1.0 and enabling pytorch 2.5 support. | ||
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| **New in version 1.1.0** | ||
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| This version introduces several improvements: | ||
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| - Bugfixes and several minor improvements | ||
| - Compatibility with napari >=0.5 | ||
| - Automatic instance segmentation CLI | ||
| - Initial support for parameter efficient fine-tuning and automatic semantic segmentation in 2d and 3d (not available in napari plugin, part of the python library) | ||
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| **New in version 1.0.1** | ||
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| Use stable URL for model downloads and fix issues in state precomputation for automatic segmentation. | ||
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| **New in version 1.0.0** | ||
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| This release mainly fixes issues with the previous release and marks the napari user interface as stable. | ||
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| **New in version 0.5.0** | ||
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| 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. | ||
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| **New in version 0.4.1** | ||
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| - Bugfix for the image series annotator. Before the automatic segmentation did not work correctly. | ||
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| **New in version 0.4.0** | ||
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| - Significantly improved model finetuning | ||
| - Update the finetuned models for microscopy, see [details in the doc](https://computational-cell-analytics.github.io/micro-sam/micro_sam.html#finetuned-models) | ||
| - Training decoder for direct instance segmentation (not available via the GUI yet) | ||
| - Refactored model download functionality using [pooch](https://pypi.org/project/pooch/) | ||
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| **New in version 0.3.0** | ||
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| - Support for ellipse and polygon prompts | ||
| - Support for automatic segmentation in 3d | ||
| - Training refactoring and speed-up of fine-tuning | ||
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| **New in version 0.2.1 and 0.2.2** | ||
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| - Several bugfixes for the newly introduced functionality in 0.2.0. | ||
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| **New in version 0.2.0** | ||
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| - 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 | ||
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| **New in version 0.1.1** | ||
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| - Fine-tuned segment anything models for microscopy (experimental) | ||
| - Simplified instance segmentation menu | ||
| - Menu for clearing annotations | ||
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| **New in version 0.1.0** | ||
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| - 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_annotator` for 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. | ||
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| **New in version 0.0.2** | ||
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| - 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. |