{"name":"unet-lungs-segmentation","display_name":"Mouse lungs segmentation","visibility":"public","icon":"","categories":[],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"unet-lungs-segmentation.predict","title":"Lungs segmentation","python_name":"unet_lungs_segmentation.napari_plugin:LungsSegmentationWidget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"unet-lungs-segmentation.predict","display_name":"Lungs segmentation","autogenerate":false}],"sample_data":[{"key":"lung_ct","display_name":"Mouse lung CT scan","uri":"https://zenodo.org/record/8099852/files/lungs_ct.tif","reader_plugin":null}],"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"unet-lungs-segmentation","version":"1.0.9","dynamic":null,"platform":null,"supported_platform":null,"summary":"3D U-Net model for the segmentation of the lungs in mice CT scans.","description":"# 🐭 Lungs segmentation in mice CT scans\n\nWe provide a neural network model for segmenting the lungs of the mice. The model is based on the [U-Net](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/) architecture.\n\n

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\n\nThe goal of our tool is to provid a reliable way to segment the lungs in mouse CT scans. The U-net model produces a binary mask representing the segmentation of the lungs.\n\n## Try the model \n\n- [Install the package](#installation)\n- [Follow the usage instructions](#usage-in-napari)\n\n## Installation\n\nWe recommend performing the installation in a clean Python environment.\n\nThe code requires `python>=3.9`, as well as `pytorch>=2.0`. Please install Pytorch first and separately following the instructions for your platform on [pytorch.org](https://pytorch.org/get-started/locally/).\n\nInstall `unet_lungs_segmentation` using *pip* after you've installed Pytorch:\n\n```sh\npip install unet_lungs_segmentation\n```\n\nor clone the repository and install with:\n\n```sh\ngit clone https://github.com/qchapp/lungs-segmentation.git\npip install -e .\n```\n\n## Models\n\nThe model weights (~1 GB) will be automatically downloaded from [Hugging Face](https://huggingface.co/qchapp/unet-lungs-segmentation-weights).\n\n\n## Usage in Napari\n\n[Napari](https://napari.org/stable/) is a multi-dimensional image viewer for python. To use our model in Napari, start the viewer with\n\n```sh\nnapari\n```\n\nTo open an image, use `File > Open files` or drag-and-drop an image into the viewer window. If you want to open medical image formats such as NIFTI directly, consider installing the [napari-medical-image-formats](https://pypi.org/project/napari-medical-image-formats/) plugin.\n\n**Sample data**: To test the model, you can run it on our provided sample image. In Napari, open the image from `File > Open Sample > Mouse lung CT scan`.\n\nNext, in the menu bar select `Plugins > Lungs segmentation (unet_lungs_segmentation)`. Select an image and run it by pressing the \"Segment lungs\" button.\n\n

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\n\n## Usage as a library\n\nYou can run a model in just a few lines of code to produce a segmentation mask from an image (represented as a numpy array).\n\n```py\nfrom unet_lungs_segmentation import LungsPredict\n\nlungs_predict = LungsPredict()\nmask = lungs_predict.segment_lungs(your_image)\n```\nor if you want to apply a specific `threshold` (`float` between 0 and 1):\n```py\nmask = lungs_predict.segment_lungs(your_image, threshold)\n```\n\n## Usage as a CLI\n\nRun inference on an image from the command-line. For example:\n\n```sh\nuls_predict_image -i /path/to/folder/image_001.tif [-t ]\n```\n\nThe `` will be applied to the predicted image in order to have a binary mask. A default threshold of 0.5 will be applied if none is given. Should be a `float` between 0 and 1.\n\nThe command will save the segmentation next to the image:\n```\nfolder/\n ├── image_001.tif\n ├── image_001_mask.tif\n```\n\nRun inference in batch on all images in a folder:\n\n```sh\nuls_predict_folder -i /path/to/folder/ [-t ]\n```\nWill produce:\n```\nfolder/\n ├── image_001.tif\n ├── image_001_mask.tif\n ├── image_002.tif\n ├── image_002_mask.tif\n```\n\n## Dataset\n\nOur model was trained using a dataset of `355` images coming from 17 different experiments, 2 different scanners and validated on `62` images.\n\n## Issues\n\nIf you encounter any problems, please fill an issue along with a detailed description.\n\n## License\n\nThis model is licensed under the [BSD-3](LICENSE.txt) license.\n\n## Carbon footprint of this project\n\nAs per the online tool [*Green algorithms*](http://calculator.green-algorithms.org/), the footprint of training this model was estimated to be around 584 g CO2e.\n","description_content_type":"text/markdown","keywords":null,"home_page":"https://github.com/qchapp/lungs-segmentation.git","download_url":null,"author":"Quentin Chappuis, Center for Imaging, Ecole Polytechnique Federale de Lausanne (EPFL)","author_email":"quentin.chappuis@epfl.ch","maintainer":null,"maintainer_email":null,"license":"BSD-3-Clause","classifier":["Development Status :: 2 - Pre-Alpha","Framework :: napari","Intended Audience :: Developers","License :: OSI Approved :: BSD License","Operating System :: OS Independent","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Topic :: Scientific/Engineering :: Image Processing"],"requires_dist":["magicgui","qtpy","napari[all]>=0.4.16","napari-label-focus","tifffile","scikit-image","matplotlib","csbdeep","python-dotenv","huggingface-hub","tox; extra == \"testing\"","pytest; extra == \"testing\"","pytest-cov; extra == \"testing\"","pytest-qt; extra == \"testing\"","napari; extra == \"testing\"","pyqt5; extra == \"testing\""],"requires_python":">=3.8","requires_external":null,"project_url":["Source Code, https://github.com/qchapp/lungs-segmentation.git"],"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}