Version 1.2.10

• MeasureSkeleton now applies coordinates to "skeleton" objects as well as "edge" and "junction" objects. Skeletons get the same coordinates as edges and junctions.
• Added Javadoc comments to modules and module parameters
• Added guide for running MIA in headless mode on Mac
• Added ability to manually add modules to list of available modules. This is useful when developing new modules (can add new module from main method when running from IDE).
• Updated README.md with information about contributing
• Fixed broken links in documentation.

Version 1.2.9

• Moved documentation to separate mianalysis.github.io repository. This will automatically compile after each release using GitHub Actions.
• Modified usage of SubHyperStackMaker to account for different operation in latest version of ImageJ.
• Modified test images for 16 bit intensity inversion to match current ImageJ default behaviour.

Version 1.2.8

• RelateOneToOne now has the option to only consider linking objects in the same timepoint.
• Use of BioFormats' Memoizer is now optional (controlled via Edit > Preferences) and included check for Kryo dependency. If Kryo isn't version 5.4.0 or higher, Memoizer will be automatically disabled as it would fail to run otherwise.
• Added module dependency for ApplyDeepImageJModel, specifying for now that DeepImageJ needs to be version 2.1.15 or lower. Integration with DeepImageJ version 3 and above will be added in the coming weeks (targeted for end of August 2023).
• Updated README.md to include details of preprint and poster

Version 1.2.7

• ExtractObjectEdges now accepts object and parent object measurements as metrics for the edge width
• FilterMeasurementsByExtremes can now retain/remove N objects with the largest/smallest measurements, rather than a single object
• Added ClearOverlay module to remove any overlay components from an image
• Added MIA_RunWorkflow macro to run a workflow directly from an ImageJ macro without the need for MIA to be open
• Other general bug fixes

Version 1.2.6

• Added "Random (vibrant)" colormap for overlays
• Bugfix for splash screen on MacOS
• Other general bugfixes

Version 1.2.5

• AddLabels can now use positions from object measurements
• Added MorphoLibJ's surface area and sphericity measurements to output of MeasureObjectShape
• Added measurement outputs to RealteOneToOne
• Updated XLSX exporter dependencies so errors and warning shouldn't occur

Version 1.2.4

• Image registration modules now work with images of different resolutions. Both images are padded to the maximum width/height of either image.

Version 1.2.3

• Added bit depth as a measurement for MeasureImageDimensions
• Overlays in ManuallyIdentifyObjects now only appear in their Z-slice
• Added option to MaskObjects to remove objects left with no volume following masking
• Added alternative thresholding option to RelateManyToMany to allow linking as long as one object has a minimum overlap.

Version 1.2.2

• Added in ellipsoid fitting using a couple of BoneJ functions
• Added variable patch size for ApplyDeepImageJ
• General optimisations and bug fixes

Version 1.2.1

• Added ApplyDeepImageJModel module, which runs any DeepImageJ currently installed in Fiji on a provided image (requires DeepImageJ update site to be enabled).
• Added DistanceBands module, which creates concentric band objects around objects in a binarised image
• Added GrowObjects module, which simplifies the application of segmenting cells and cytoplasm. This can perform distance- or intensity-based watershed segmentation of a region from provided marker objects. The output objects are automatically related to their marker object.
• Added more automated tests, primarily for image processing modules.
• SaveObjectsAsROIs now includes additional options for saving objects as individual ROI files or on a per-slice basis.
• Generalised image rendering for improved compatibility with other projects that use MIA as a library.
• General bug fixes and improvements.
• Updated publications on https://mianalysis.github.io/mia/publications site

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Example image generated using DeepImageJ integration (model is Neuron Segmentation in EM (Membrane Prediction) by Constantin Pape)