{"name":"napari-findaureus","display_name":"napari-findaureus","visibility":"public","icon":"","categories":[],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-findaureus.get_reader","title":"Open data to locate bacteria","python_name":"findaureus._reader:napari_get_reader","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-findaureus.make_magic_widget","title":"napari-findaureus","python_name":"findaureus._widget:Find_Bacteria","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":[{"command":"napari-findaureus.get_reader","filename_patterns":["*.czi","*.nd2","*.lif","*.tiff"],"accepts_directories":false}],"writers":null,"widgets":[{"command":"napari-findaureus.make_magic_widget","display_name":"napari-findaureus","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"napari-findaureus","version":"1.0.0","dynamic":["license-file"],"platform":null,"supported_platform":null,"summary":"Locate bacteria in CLSM obtained infected bone tissue images","description":"# napari-findaureus\n\nnapari-findaureus is a napari plugin that locates *Staphylococcus aureus* (bacteria) in fluorescence-labelled infected bone tissue images acquired with confocal laser scanning microscopy (CLSM).\n\nCompatibility: Python 3.9–3.11 and recent napari releases. The package aims to work with modern napari/Qt stacks; if you encounter compatibility issues, consider creating a fresh environment with a supported Python version.\n<p align=\"center\">\n<img src=\"docs/napari-findaureus.png\" />\n</p>\n\nFindaureus is a tool designed to identify bacteria in infected bone tissue images obtained via Confocal Laser Scanning Microscopy (CLSM). This tool can be accessed independently [here](https://github.com/shibarjun/Findaureus). Findaureus has been integrated as a plugin for napari. In addition to its bacteria-locating algorithm, the napari viewer provides enhanced 2D and 3D visualization, region-of-interest-based bacteria finding with relevant stats in the widget, and an `Export results` button to save analysis summaries.\n\n----------------------------------\n## Installation\nRecommended: create a fresh conda environment using Python 3.11 and install napari in that environment.\n\nLinux / Windows:\n\n```bash\nconda create -n napari-findaureus python=3.11\nconda activate napari-findaureus\npip install \"napari[all]\" napari-findaureus\n```\n\nmacOS:\n\n```bash\nconda create -n napari-findaureus python=3.11\nconda activate napari-findaureus\nconda install -c conda-forge pyqt\npip install \"napari[all]\" napari-findaureus\n```\n\nIf you do not already have a suitable test image, download sample data from [Zenodo](https://zenodo.org/doi/10.5281/zenodo.8411791)\n\n## Using napari-findaureus\n1. Launch napari from the activated environment:\n\n```bash\nnapari\n```\n\n2. Open your image in napari using `File > Open` or by drag-and-drop.\n   - If you do not have an image, download it from Zenodo and open it in napari.\n\n3. Open `Plugins > napari-findaureus` to display the plugin widget.\n\n4. From the napari `layer list` panel select the correct bacteria channel or layer for analysis.\n\n5. Optional ROI analysis:\n   - Draw a region of interest using the `Shapes` layer.\n   - The analysis will be restricted to the selected ROI when a shape is active.\n\n6. Click `Find bacteria!` to run the detection.\n\n7. Review the results:\n   - `Bacteria mask` layer for pixel-wise segmentation\n   - `Bounding box` layer for detected objects\n   - Counts and area measurements per Z-plane and for the selected ROI or full image\n   - Use the `Export results` button (below the Analysis Results box) to save a compact TXT or CSV containing image info and per‑Z‑plane bacteria counts and areas.\n\n8. Use `Reset` to clear results before analyzing another image or ROI.\n\n9. Use the `Instruction` button in the widget for built-in guidance.\n\n----------------------------------\n## Contributing\nWe welcome and appreciate all contributions to the `napari-findaureus` project! Whether it's reporting bugs, suggesting new features, improving documentation, or writing code, your involvement is greatly valued.\nWhen using our dataset or referring to our work, we kindly ask that you acknowledge the dataset and cite the related articles. This helps support our work and allows us to continue improving this project.\n\nThank you for your interest and support!\n## Citations and Dataset\n### Findaureus\n Mandal S, Tannert A, Löffler B, Neugebauer U, Silva LB (2024) [Findaureus: An open-source application for locating Staphylococcus aureus in fluorescence-labelled infected bone tissue slices.](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296854) PLoS ONE 19(1): e0296854.\n### Infected mouse bone tissue\nMandal S, Tannert A, Ebert C, Guliev RR, Ozegowski Y, Carvalho L, Wildemann B, Eiserloh S, Coldewey SM, Löffler B, Bastião Silva L, Hoerr V, Tuchscherr L, Neugebauer U. (2023) [Insights into S. aureus-Induced Bone Deformation in a Mouse Model of Chronic Osteomyelitis Using Fluorescence and Raman Imaging.](https://www.mdpi.com/1422-0067/24/11/9762) International Journal of Molecular Sciences 24(11):9762.\n\n### [Dataset](https://zenodo.org/doi/10.5281/zenodo.8411791)\n## Acknowledgements\n\nThis project is a part of the European Union's Horizon 2020 research and innovation program under grant agreement No 861122 (ITN IMAGE-IN). We acknowledge support from the Jena Biophotonics and Imaging Laboratory (JBIL), from the European Union via EFRE funds within the Thüringer Innovationszentrum für Medizintechnik-Lösungen (ThIMEDOP, FKZ IZN 2018 0002), the BMBF via the funding program Photonics Research Germany (LPI, FKZ: 13N15713) and via the CSCC (FKZ 01EO1502) and the Institute of Anatomical and Molecular Pathology, University Coimbra, Portugal.\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":"Shibarjun Mandal","author_email":"shibarjunmandal@gmail.com","maintainer":null,"maintainer_email":null,"license":"MIT","classifier":["Development Status :: 2 - Pre-Alpha","Framework :: napari","Intended Audience :: Developers","License :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3 :: Only","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Topic :: Scientific/Engineering :: Image Processing"],"requires_dist":["magicgui>=0.3","qtpy>=2.3","napari[all]","aicsimageio>=4.11","nd2>=0.5","aicspylibczi>=3.1","fsspec>=2023.5","readlif>=0.6","czifile>=2019.7","tifffile>=2023.7","webcolors>=1.13","opencv-python>=4.7","numpy>=1.24","scikit-image>=0.20","xmltodict","tox; extra == \"testing\"","pytest; extra == \"testing\"","pytest-cov; extra == \"testing\"","pytest-qt; extra == \"testing\"","napari; extra == \"testing\"","pyqt5; extra == \"testing\""],"requires_python":"<4,>=3.9","requires_external":null,"project_url":null,"provides_extra":["testing"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}