{"name":"napari-tmidas","display_name":"T-MIDAS","visibility":"public","icon":"","categories":["Annotation","Segmentation","Acquisition"],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-tmidas.get_reader","title":"Open data with T-MIDAS","python_name":"napari_tmidas._reader:napari_get_reader","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-tmidas.write_multiple","title":"Save multi-layer data with T-MIDAS","python_name":"napari_tmidas._writer:write_multiple","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-tmidas.write_single_image","title":"Save image data with T-MIDAS","python_name":"napari_tmidas._writer:write_single_image","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari-tmidas.make_sample_data","title":"Load sample data from 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Anything","autogenerate":false},{"command":"napari-tmidas._roi_colocalization","display_name":"Batch ROI Colocalization Analysis","autogenerate":false},{"command":"napari-tmidas._label_based_cropping","display_name":"Label-Based Image Cropping","autogenerate":false},{"command":"napari-tmidas._frame_removal","display_name":"Frame Removal Tool","autogenerate":false}],"sample_data":[{"command":"napari-tmidas.make_sample_data","key":"unique_id.1","display_name":"T-MIDAS"}],"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"napari-tmidas","version":"0.5.4","dynamic":["license-file"],"platform":null,"supported_platform":null,"summary":"A plugin for batch processing of confocal and whole-slide microscopy images of biological tissues","description":"# napari-tmidas\n\n[![License BSD-3](https://img.shields.io/pypi/l/napari-tmidas.svg?color=green)](https://github.com/macromeer/napari-tmidas/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/napari-tmidas.svg?color=green)](https://pypi.org/project/napari-tmidas)\n[![Python Version](https://img.shields.io/pypi/pyversions/napari-tmidas.svg?color=green)](https://python.org)\n[![Downloads](https://static.pepy.tech/badge/napari-tmidas)](https://pepy.tech/project/napari-tmidas)\n[![GitHub stars](https://img.shields.io/github/stars/MercaderLabAnatomy/napari-tmidas?style=social)](https://github.com/MercaderLabAnatomy/napari-tmidas/)\n[![DOI](https://zenodo.org/badge/943353883.svg)](https://doi.org/10.5281/zenodo.17988815)\n[![tests](https://github.com/macromeer/napari-tmidas/workflows/tests/badge.svg)](https://github.com/macromeer/napari-tmidas/actions)\n\n\n\n**Need fast batch processing for confocal & whole-slide microscopy images of biological cells and tissues?**\n\nThis open-source napari plugin integrates state-of-the-art AI + analysis tools in an interactive GUI with side-by-side result comparison! Transform, analyze, and quantify microscopy data at scale including deep learning - from file conversion to segmentation, tracking, and analysis.\n\n![napari-tmidas-interactive-table-example](https://github.com/user-attachments/assets/1330cc6c-18de-46f4-a7ef-e1d7ffc3970e)\n\n\n## ✨ Key Features\n\n🤖 **AI Methods Built-In**\n- Virtual staining (VisCy) • Denoising (CAREamics) • Spot detection (Spotiflow) • Segmentation (Cellpose, Convpaint) • Tracking (Trackastra, Ultrack)\n- Auto-install in isolated environments • No dependency conflicts • GPU acceleration\n\n🔄 **Universal File Conversion**\n- Convert LIF, ND2, CZI, NDPI, Acquifer → TIFF or OME-Zarr\n- Preserve spatial metadata automatically\n\n⚡ **Batch Processing**\n- Process entire folders with one click • 40+ processing functions • Progress tracking & quality control\n\n� **Interactive Workflow**\n- Side-by-side table view of original and processed images • Click to instantly compare results • Quickly iterate parameter values • Real-time visual feedback\n\n�📊 **Complete Analysis Pipeline**\n- Segmentation → Tracking → Quantification → Colocalization\n\n## 🚀 Quick Start\n\n```bash\n# Install napari and the plugin\nmamba create -y -n napari-tmidas -c conda-forge python=3.11\nmamba activate napari-tmidas\npip install \"napari[all]\"\npip install napari-tmidas\n\n# Launch napari\nnapari\n```\n\nThen find napari-tmidas in the **Plugins** menu. [Watch video tutorials →](https://www.youtube.com/@macromeer/videos)\n\n> **💡 Tip**: AI methods (SAM2, Cellpose, Spotiflow, etc.) auto-install into isolated environments on first use - no manual setup required!\n\n## 📖 Documentation\n\n### AI-Powered Methods\n\n| Method | Description | Documentation |\n|--------|-------------|---------------|\n| 🎨 **VisCy** | Virtual staining from phase/DIC | [Guide](docs/viscy_virtual_staining.md) |\n| 🔧 **CAREamics** | Noise2Void/CARE denoising | [Guide](docs/careamics_denoising.md) |\n| 🎯 **Spotiflow** | Spot/puncta detection | [Guide](docs/spotiflow_detection.md) |\n| 🔬 **Cellpose** | Cell/nucleus segmentation | [Guide](docs/cellpose_segmentation.md) |\n| 🎨 **Convpaint** | Custom semantic/instance segmentation | [Guide](docs/convpaint_prediction.md) |\n| 📈 **Trackastra** | Transformer-based cell tracking | [Guide](docs/trackastra_tracking.md) |\n| 🔗 **Ultrack** | Cell tracking based on segmentation ensemble | [Guide](docs/ultrack_tracking.md) |\n\n### Core Workflows\n\n- **[File Conversion](docs/file_conversion.md)** - Multi-format microscopy file conversion (LIF, ND2, CZI, NDPI, Acquifer)\n- **[Batch Processing](docs/basic_processing.md)** - Label operations, filters, channel splitting\n- **[Frame Removal](docs/frame_removal.md)** - Interactive human-in-the-loop frame removal from time series\n- **[Label-Based Cropping](docs/label_based_cropping.md)** - Interactive ROI extraction with label expansion\n- **[Quality Control](docs/grid_view_overlay.md)** - Visual QC with grid overlay\n- **[Quantification](docs/regionprops_analysis.md)** - Extract measurements from labels\n- **[Colocalization](docs/advanced_processing.md#colocalization-analysis)** - Multi-channel ROI analysis\n\n### Advanced Features\n\n- [Batch Crop Anything](docs/crop_anything.md) - Interactive object cropping with SAM2\n- [Batch Label Inspection](docs/batch_label_inspection.md) - Manual label verification and editing\n- [SciPy Filters](docs/advanced_processing.md#scipy-filters) - Gaussian, median, morphological operations\n- [Scikit-Image Filters](docs/advanced_processing.md#scikit-image-filters) - CLAHE, thresholding, edge detection\n\n## 💻 Installation\n\n### Step 1: Install napari\n\n```bash\nmamba create -y -n napari-tmidas -c conda-forge python=3.11\nmamba activate napari-tmidas\npython -m pip install \"napari[all]\"\n```\n\n### Step 2: Install napari-tmidas\n\n| Your Needs | Command |\n|----------|---------|\n| **Standard installation** | `pip install napari-tmidas` |\n| **Want the latest dev features** | `pip install git+https://github.com/MercaderLabAnatomy/napari-tmidas.git` |\n\n## 🖼️ Screenshots\n\n<details>\n<summary><b>File Conversion Widget</b></summary>\n\n<img src=\"https://github.com/user-attachments/assets/e377ca71-2f30-447d-825e-d2feebf7061b\" alt=\"File Conversion\" width=\"600\">\n\nConvert proprietary formats to open standards with metadata preservation.\n</details>\n\n<details>\n<summary><b>Batch Processing Interface</b></summary>\n\n<img src=\"https://github.com/user-attachments/assets/cfe84828-c1cc-4196-9a53-5dfb82d5bfce\" alt=\"Batch Processing\" width=\"600\">\n\nSelect files → Choose processing function → Run on entire dataset.\n</details>\n\n<details>\n<summary><b>Label Inspection</b></summary>\n\n<img src=\"https://github.com/user-attachments/assets/0bf8c6ae-4212-449d-8183-e91b23ba740e\" alt=\"Label Inspection\" width=\"600\">\n\nInspect and manually correct segmentation results.\n</details>\n\n<details>\n<summary><b>SAM2 Crop Anything</b></summary>\n\n<img src=\"https://github.com/user-attachments/assets/6d72c2a2-1064-4a27-b398-a9b86fcbc443\" alt=\"Crop Anything\" width=\"600\">\n\nInteractive object selection and cropping with SAM2.\n</details>\n\n## 📋 TODO\n\n### Memory-Efficient Zarr Streaming\n\n**Current Limitation**: Processing functions pre-allocate full output arrays in memory before writing to zarr. For large TZYX time series (e.g., 100 timepoints × 1024×1024×20), this requires ~8+ GB peak memory even when using zarr output.\n\n**Planned Enhancement**: Implement incremental zarr writing across all processing functions:\n- Process one timepoint at a time\n- Write directly to zarr array on disk\n- Keep only single timepoint in memory (~80 MB vs 8 GB)\n- Maintain OME-Zarr metadata and chunking\n\n**Impact**: Enable processing of arbitrarily large time series limited only by disk space, not RAM. Critical for high-throughput microscopy workflows.\n\n**Affected Functions**: Convpaint prediction, Cellpose segmentation, CAREamics denoising, VisCy virtual staining, Trackastra tracking, and all batch processing operations with zarr output.\n\n## 🤝 Contributing\n\nContributions are welcome! Please ensure tests pass before submitting PRs:\n\n```bash\npip install tox\ntox\n```\n\n## 📄 License\n\nBSD-3 License - see [LICENSE](LICENSE) for details.\n\n## 🐛 Issues\n\nFound a bug or have a feature request? [Open an issue](https://github.com/MercaderLabAnatomy/napari-tmidas/issues)\n\n## 🙏 Acknowledgments\n\nBuilt with [napari](https://github.com/napari/napari) and powered by:\n\n**AI/ML Methods:**\n- [Cellpose](https://github.com/MouseLand/cellpose) • [Convpaint](https://github.com/guiwitz/napari-convpaint) • [VisCy](https://github.com/mehta-lab/VisCy) • [CAREamics](https://github.com/CAREamics/careamics) • [Spotiflow](https://github.com/weigertlab/spotiflow) • [Trackastra](https://github.com/weigertlab/trackastra) • [Ultrack](https://github.com/royerlab/ultrack) • [SAM2](https://github.com/facebookresearch/segment-anything-2)\n\n**Core Scientific Stack:**\n- [NumPy](https://numpy.org/) • [scikit-image](https://scikit-image.org/) • [PyTorch](https://pytorch.org/)\n\n**File Format Support:**\n- [OME-Zarr](https://github.com/ome/ome-zarr-py) • [tifffile](https://github.com/cgohlke/tifffile) • [nd2](https://github.com/tlambert03/nd2) • [pylibCZIrw](https://github.com/ZEISS/pylibczi) • [readlif](https://github.com/nimne/readlif)\n\n---\n\n[PyPI]: https://pypi.org/project/napari-tmidas\n[pip]: https://pypi.org/project/pip/\n[tox]: https://tox.readthedocs.io/en/latest/\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":"Marco Meer","author_email":"marco.meer@pm.me","maintainer":null,"maintainer_email":null,"license":"Copyright (c) 2025, Marco Meer\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\n* Neither the name of copyright holder nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n","classifier":["Development Status :: 2 - Pre-Alpha","Framework :: napari","Intended Audience :: Developers","License :: OSI Approved :: BSD License","Operating System :: MacOS","Operating System :: POSIX :: Linux","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3 :: Only","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Topic :: Scientific/Engineering :: Image Processing"],"requires_dist":["numpy<2.1,>=1.23.0","magicgui","tqdm","qtpy","scikit-image>=0.19.0","scikit-learn-extra>=0.3.0","pyqt5","zarr","ome-zarr","napari-ome-zarr","nd2","pylibCZIrw","readlif","tifffile<2025.5.21,>=2023.7.4","tiffslide","acquifer-napari","psygnal>=0.9.0","zarr>=2.16.0","ome-zarr>=0.8.0","tox; extra == \"testing\"","pytest>=7.0.0; extra == \"testing\"","pytest-cov; extra == \"testing\"","pytest-qt; extra == \"testing\"","pytest-timeout; extra == \"testing\"","napari; extra == \"testing\"","pyqt5; extra == \"testing\"","psygnal>=0.8.0; extra == \"testing\"","scikit-learn-extra>=0.3.0; extra == \"testing\"","scikit-learn-extra>=0.3.0; extra == \"clustering\"","napari-tmidas[clustering,testing]; extra == \"all\""],"requires_python":">=3.10","requires_external":null,"project_url":["Bug Tracker, https://github.com/macromeer/napari-tmidas/issues","Documentation, https://github.com/macromeer/napari-tmidas#README.md","Source Code, https://github.com/macromeer/napari-tmidas","User Support, https://github.com/macromeer/napari-tmidas/issues"],"provides_extra":["testing","clustering","all"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}