Cellular infection phenotyping using annotated viral sensor data & label-free images #70
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new black version has different rules
* inital commit adding the normalization. * adding dataset_statistics to each fov to facilitate the configurable augmentations * fix indentation * ruff * test preprocessing * remove redundant field * cleanup --------- Co-authored-by: Ziwen Liu <[email protected]>
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applications/infection_classification/predict_infection_classifier.py
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The goal of the analysis is to identify infected and uninfected cells in a FOV using either viral sensor fluorescence or label-free images with nuclear instance segmentation label to output a nuclear label identifying infected cells. The value of the nuclear label for each cell will indicate the following:
0: background
1: uninfected
2: Infected
3: unidentified (cases where the sensor has started relocalization, but the expression is not good enough to categorize as the infected state).
A manually annotated dataset with the above criteria is available here:
/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/datasets/Exp_2023_09_28_DENV_A2_infMarked.zarrThe code is added to examples/infection_phenotyping folder on the branch.
infection_annotator.pyuses napari to help annotate the nuclear label.infection_classification_model.pyis a work in progress on building a model for translation of instance segmentation of nuclear label into infection annotated label using the viral sensor fluorescence images or the label-free images.