{"name":"napari-UniSPAC","display_name":"napari-UniSPAC","visibility":"public","icon":null,"categories":[],"schema_version":"0.2.1","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari-UniSPAC.seg","title":"UniSPAC","python_name":"napari_UniSPAC._widget:UniSPACwidget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"napari-UniSPAC.seg","display_name":"UniSPAC","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.4","name":"napari-UniSPAC","version":"1.1.0","dynamic":["license-file"],"platform":null,"supported_platform":null,"summary":"A Unified Segmentation framework for Proofreading and Annotation in Connectomics (UniSPAC)!","description":"# napari-UniSPAC\n\n`napari-UniSPAC` is a napari plugin for UniSPAC, a unified segmentation framework for proofreading and annotation in connectomics. The plugin provides interactive 2D and 3D neuron segmentation workflows for volume electron microscopy data, including click-based proofreading, 3D tracing, and automatic segmentation modes.\n\n## Overview\n\nUniSPAC is designed for efficient neuron segmentation and proofreading with sparse user interaction. In napari, users can load an image volume, select an output labels layer, load the pretrained checkpoints, and refine segmentations with mouse clicks.\n\nMain features:\n\n- Interactive click-based 2D neuron segmentation\n- 3D tracing across adjacent slices\n- Automatic \"Everything\" segmentation mode\n- Optional online adaptation of the 2D mask head\n- Checkpoint loading from a user-selected local folder\n\n## System Requirements\n\nThe plugin requires a Python environment with napari and PyTorch. GPU acceleration is strongly recommended for practical use.\n\nRecommended hardware:\n\n- NVIDIA GPU with CUDA support\n\nRecommended software:\n\n- Python 3.9 or later\n- napari\n- PyTorch installed for the local CUDA or CPU configuration\n\n## Installation\n\nInstall napari:\n\n```shell\npip install -U 'napari[all]'\n```\n\nClone and install this plugin:\n\n```shell\ngit clone https://github.com/ddd9898/napari-UniSPAC.git\ncd napari-UniSPAC\npip install -e .\n```\n\nInstall the additional runtime dependencies listed in `requirements.txt` when needed:\n\n```shell\npip install -r requirements.txt\n```\n\nPyTorch should be installed separately using the command recommended for your CUDA or CPU setup at [pytorch.org](https://pytorch.org/).\n\n## Model Checkpoints\n\nThe plugin does not download model weights automatically. Download the pretrained checkpoints manually from the UniSPAC OneDrive folder:\n\n```text\nhttps://1drv.ms/f/c/88a3ba3c5aa53eeb/IgBDDnxcSN9QRKU-3jNDaBF4AdMLvpbbU3FTvKO2ngsWDlQ?e=XtkNDe\n```\n\nDownload the following four files from the `leave-human-out` experiment and place them in the same local folder:\n\n```text\nACRLSD_2D_leaveout_human_holdoutVal16_neo_Best_in_val.model\nACRLSD_3D_leaveout_human_holdoutVal16_neo_preLSD_Best_in_val.model\nsegEM2d_plus_leaveout_human_holdoutVal16_neo_wb1.0_wd1.0_wbd0.3_mtp5_sd0.55_Best_in_val.model\nsegEM3d_trace_leaveout_human_holdoutVal16_neo_wb1.0_wd1.0_wbd0.3_Best_in_val.model\n```\n\nKeep all four files together so the plugin can resolve the referenced backbones during model loading.\n\n## Usage\n\nStart napari:\n\n```shell\nnapari\n```\n\nIn napari:\n\n1. Open an image or volume.\n2. Open the UniSPAC plugin from the napari plugin menu.\n3. Click `Load from folder` and select the local folder containing the four checkpoint files.\n4. Select the input image layer and output labels layer.\n5. Click `Activate` for interactive proofreading, or select `Everything` mode to run automatic segmentation.\n\n## Repository Contents\n\n- `src/napari_UniSPAC/`: napari plugin source code\n- `requirements.txt`: runtime dependency list\n- `README.md`: installation and usage instructions\n- `LICENSE`: software license\n\nReference scripts and local experimental files may be present in development copies of the repository, but they are not required to run the napari plugin.\n\n## Data and Model Availability\n\nPretrained UniSPAC checkpoints are distributed separately through the OneDrive link above. Example image data and additional experiment outputs are not bundled with the Python package.\n\n## Citation\n\nIf you use UniSPAC or this napari plugin in a publication, please cite the associated UniSPAC manuscript or repository. Citation details will be updated here when the final reference is available.\n\n## License\n\nThis project is distributed under the terms described in `LICENSE`.\n","description_content_type":null,"keywords":null,"home_page":"https://github.com/ddd9898/UniSPAC","download_url":null,"author":null,"author_email":null,"maintainer":null,"maintainer_email":null,"license":null,"classifier":["Framework :: napari"],"requires_dist":["napari"],"requires_python":null,"requires_external":null,"project_url":null,"provides_extra":null,"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}