Machine learning tool for polyploid independent population estimates for endosymbionts. The included dataset works to phenotype Buchnera and aphid bacteriocytes in DAPI stained confocal microscope images. Please read ##Ref for information
To use Buchnera dataset, use this colab: https://colab.research.google.com/drive/1YZlQgm9Hf4qAFVPvb6JbNHlYyvS2cNN4#scrollTo=AcLEB5XJpH0b, 'GUI.py', and 'buchnera_metrics.py'.
To train for other endosymbiotic systems:
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Annotate images of endosymbiotic tissue using Labelme (https://github.com/wkentaro/labelme)
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Divide images and annotations into tiles using 'split_json.py'
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Randomly divide tiles into training, test, and validation datasets, and convert j
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Train maskRCNN on your datasets using Detectron2. A basic Detectron2 tutorial is here: https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5 The specific training settings we used are in 'trainer_settings.py', although these settings may need adjustments for other endosymbiotic systems i.e. non-spherical endosymbionts. Our data augmentation settings are in 'trainer_augmentation.py'.
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The resultant model weights (the '.pth' file) can be used in place of ours in a detector - see 'detector_example.py' and 'Buchnearer.py' for examples.
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Assess performance of your model - i.e. prediction & recall - and use a GUI (like 'GUI.py') to curate results. GUI may need to be altered for non-spherical endosymbionts.