What's new
Preprocessing
We introduce a new preprocessing module, to correct the background of image stacks before segmentation. The user can choose among two background correction methods.
- The first is to fit a 2D model (paraboloid, plane) to each image of the stacks where the non-homogeneous part is filtered out using a threshold on the STD-filtered image. This preprocessing was previously available in the measurement options, but the user could not export the preprocessed image. Both options are now possible.
- The second is a model-free estimate method for the background using the multi-position information to reconstruct a median background within a well. This is a typical background reconstruction method for interference microscopy images (IRM, RICM). The user can select an optimal frame range where cells are less likely to be visible, threshold the least homogeneous parts of the image with the STD-filter technique, and visualize the background reconstructed for each well. If the background is satisfying, it is applied to each image, with an automatic regression to accommodate slight focus loss and intensity variations within a well.
Preprocessing protocols can be combined to preprocess independent channels differently. The output is a new stack with the "Corrected" prefix, which can be set as the stack of interest when performing segmentation and measurements.
Threshold-based cell classification
The classifier widget has been expanded with more options to classify tracked cells. The user can now classify cells according to their most likely state (median state), assuming no state change. This option is complementary to the previously implemented "irreversible event" that was able to describe a transition between two states.
Furthermore, you can now exclude a cell class from survival analysis.
Neighborhood
We introduce a new and intuitive neighborhood method that matches touching masks as neighbors. To accommodate ambiguities in cell-cell contacts, this type of neighborhood has a tolerance parameter: the border size dilation applied to the masks before neighborhood computation. The counting methods remain strictly identical to the isotropic distance method, introduced previously.
Log
Experimental: a logger is introduced with this version. A json is written in each position folder to keep track of how a position is processed.
Viewers
A viewer class is introduced to gradually replace all image viewers in Celldetective (except for napari). Several new viewers have been introduced to facilitate the setting of analysis parameters:
- a viewer next to the position list, in the control panel header, enables to preview the current stack
- several viewers help set threshold parameters a view preprocessing steps
- a viewer was augmented to set the contour distance in the measurement options
- a viewer was augmented to represent a circle of radius controlled with a slider to set an isotropic neighborhood or estimate cell size when calling a pre-trained Cellpose model
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The viewers are accompanied by a scalebar either in µm or pixel depending on the purpose of the viewer.
Maintenance
- unitary tests for a large part of the Celldetective core
- code refactoring and layout subclasses
- widget behavior fixes, less crashes
- introduction of searchable combo boxes from superqt and colormap combo boxes
- bug fixes
Contributors
* ksenijader
* remyeltorro
**Full Changelog**: https://github.com/remyeltorro/celldetective/compare/v1.0.2...v1.1.0