Package: napari-PHILOW
napari-PHILOWPHILOWPython-based platform forhuman-in-the-loop (HITL)workflow (PHILOW)PHILOW is an interactive deep learning-based platform for 3D datasets implemented on top ofnapariThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationInstall napari and Pytorch first.SeenapariandPytorchfor more information.You can installnapari-PHILOWviapip:pip install napari-PHILOWor clone this repositorythencd PHILOW
pip install -e .UsageLaunch naparinapariload datasetPlugins > napari-PHILOW > Annotation ModeSelect original dir : all slices must be in separate 8bit PNG and must be sequentially numbered (e.g. 000.png, 001.png ...)Select mask dir : To resume from the middle of the annotation, specify here the name of the directory containing the mask image. The directory must contain the same number of files with the same name as the original image.If you are starting a completely new annotation, you do not need to specify a directory. The directory for mask is automatically created and blank images are generated and stored.Enter a name for the label or model you want to create (e.g. mito, cristae, ...)This name will be used as the directory name of the newly created mask dir if no mask dir is specified,
and as the name of the csv file for training dataset management.Check if you want to create new dataset (new model)
When checked, if there is already a csv file for training dataset management, a new csv file with one sequential number will be generated.Start tracingcreate labelsCreate a label with the brush function.
more information →https://napari.org/tutorials/fundamentals/labels.htmlOrthogonal viewIf you want to see orthogonal view, click on the location you want to see while holding down the Shift button.The image from xy, yz, and zx will be displayed on the right side of the screen.Low confident layerIf you are in the second iteration and you are loading the prediction results, you will see a low confidence layer.This shows the area where the confidence of the prediction result is low.Use this as a reference for correction.Small object layerWe provide a small object layer to find small painted areas.This is a layer for displaying small objects.The slider widget on the left allows you to change the maximum object size to be displayed.save labelsIf you want to save your label, click the "save" button on the bottom right.select training datasetWe are providing a way to manage the dataset for use in training.If you want to use the currently displayed slice as your training data, click the 'Not Checked' button near the center left to display 'Checked'.Train and pred with your gpu machineTrainTo train on your GPU machine (or with CPU),Plugins > napari-PHILOW > TrainerSelect original dir : all slices must be in separate 8bit PNG and must be sequentially numbered (e.g. 000.png, 001.png ...)Select labels dir : all label images should be named same as original images and contains data management csv fileSelect dir for save trained modelClick on the "start training" buttonDice score and dice loss are displayed. For more detail, check the command line for the progress of training. If you want to stop in the middle, click stop button.PredictTo predict labels on your machine,Plugins > napari-PHILOW > PredicterSelect original dir : all slices must be in separate 8bit PNG and must be sequentially numbered (e.g. 000.png, 001.png ...)(Optional) Select labels dir if you want to keep labels witch were used on training, and data management csv fileSelect model dir contains hdf5 fileSelect output dir for predicted labelsUncheck the box if you DO NOT want to use TAP (Three-Axis-Prediction)Click on the "predict" buttonCheck the command line for the progress of prediction. If you want to stop in the middle, use ctrl+C.You can start the next round of annotation by selecting the merged_prediction directory as the mask dir in Annotation mode.Train and predict with Google ColabIf you don't have a GPU machine, you can use Google Colab to perform GPU-based training and prediction for free.Opentrain and predict notebookand click "Open in Colab" buttonYou can upload your own dataset to train and predict, or try it on demo dataContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theGNU GPL v3.0license,
"napari-PHILOW" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.AuthorsShogo SugaHiroki KawaiYusuke HirabayashiHow to CiteShogo Suga, Koki Nakamura, Yu Nakanishi, Bruno M Humbel, Hiroki Kawai, Yusuke Hirabayashi, An interactive deep learning-based approach reveals mitochondrial cristae topologies. PLoS Biol 21(8): e3002246.https://doi.org/10.1371/journal.pbio.3002246@article {Suga_Nakamura_Nakanishi_Humbel_Kawai_Hirabayashi_2023,
	title={An interactive deep learning-based approach reveals mitochondrial cristae topologies},
	volume={21},
	ISSN={1545-7885},
	DOI={10.1371/journal.pbio.3002246},
	number={8},
	journal={PLOS Biology},
	publisher={Public Library of Science},
	author={Suga, Shogo and Nakamura, Koki and Nakanishi, Yu and Humbel, Bruno M. and Kawai, Hiroki and Hirabayashi, Yusuke},
	year={2023},
	month={Aug},
	pages={e3002246},
	language={en}
}