{"id":37670786,"url":"https://github.com/cbica/nichart_tissue_segmentation","last_synced_at":"2026-01-16T12:04:00.105Z","repository":{"id":220210544,"uuid":"751008529","full_name":"CBICA/NiChart_Tissue_Segmentation","owner":"CBICA","description":"Brain tissue segmentation using FSL FAST and DLICV","archived":false,"fork":false,"pushed_at":"2024-02-02T23:18:46.000Z","size":34,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-09T23:13:18.306Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CBICA.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-01-31T18:52:55.000Z","updated_at":"2024-07-11T10:13:37.000Z","dependencies_parsed_at":"2024-02-03T00:27:04.555Z","dependency_job_id":null,"html_url":"https://github.com/CBICA/NiChart_Tissue_Segmentation","commit_stats":null,"previous_names":["georgeaidinis/nichart_tissue_segmentation","cbica/nichart_tissue_segmentation"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/CBICA/NiChart_Tissue_Segmentation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FNiChart_Tissue_Segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FNiChart_Tissue_Segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FNiChart_Tissue_Segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FNiChart_Tissue_Segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CBICA","download_url":"https://codeload.github.com/CBICA/NiChart_Tissue_Segmentation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FNiChart_Tissue_Segmentation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28478422,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-16T11:59:17.896Z","status":"ssl_error","status_checked_at":"2026-01-16T11:55:55.838Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-01-16T12:03:59.444Z","updated_at":"2026-01-16T12:04:00.097Z","avatar_url":"https://github.com/CBICA.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NiChart_Tissue_Segmentation\n\nBrain tissue segmentation using FSL FAST and DLICV\n\n## Overview\n\nNiChart_Tissue_Segmentation offers easy brain tissue segmantation.\n\nThis is achieved through the [DLICV](https://github.com/CBICA/DLICV) and [FAST](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST) methods.\n\nGiven an input (sMRI) scan, NiChart_Tissue_Segmentation extracts the following:\n\n1. Tissue segmentation\n2. Volumetric data (optional)\n\nThis package uses [nnUNet](https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1) (version 1) as a basis model architecture for the deep learning parts, [FAST](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST) for the tissue segmentation and various other [libraries](requirements.txt).\n\nIt is available both as an installable package, as well as a [docker container](https://hub.docker.com/repository/docker/aidinisg/nichart_tissue_segmentation/general). Please see [Installation](#installation) and [Usage](#usage) for more information on how to use it.\n\n## Installation\n\n1. Create a new conda env\n\n    ```bash\n    conda create --name NCTS python=3.8\n    conda activate NCTS\n    ```\n\n    In one command:\n\n    ```bash\n    conda create --name NCTS -y python=3.8 \u0026\u0026 conda activate NCTS\n    ```\n\n2. Clone and install NiChart_Tissue_Segmentation\n\n    ```bash\n    git clone  https://github.com/CBICA/NiChart_Tissue_Segmentation.git\n    cd NiChart_Tissue_Segmentation\n    pip install .\n    ```\n\n3. Download model from this package's release as an [artifact](https://github.com/CBICA/NiChart_Tissue_Segmentation/releases/download/0.0.0/nnUNet_model.zip)\n\n4. Run NiChart_Tissue_Segmentation. Please see [Usage](#usage) for an example.\n\n## Docker-based build\n\nThe package comes already pre-built as a [docker container](https://hub.docker.com/repository/docker/aidinisg/nichart_tissue_segmentation/general), for convenience. Please see [Usage](#usage) for more information on how to use it. Alternatively, you can build the docker image locally using the dockerfile provided, like so:\n\n```bash\ndocker build -t nichart_tissue_segmentation .\n```\n\n## Usage\n\nPre-trained nnUNet models for the skull-stripping task can be found in the [NiChart_Tissue_Segmentation - 0.0.0](https://github.com/CBICA/NiChart_Tissue_Segmentation/releases/tag/0.0.0) release as an [artifact](https://github.com/CBICA/NiChart_Tissue_Segmentation/releases/download/0.0.0/nnUNet_model.zip). Feel free to use it under the package's [license](LICENSE).\n\nThe model provided as an artifact is already in the file structure that's needed for the package to work, so make sure to include it as downloaded.\n\nGiven the following file structure:\n\n```bash\ntemp\n├── in                      // Input folder. Image names are irrelevant.\n│   ├── image1.nii.gz\n│   ├── image2.nii.gz\n│   └── image3.nii.gz\n├── nnUNet_model            // As provided from the release\n│   └── nnUNet\n└── out                     // Output destination\n    ├── image1_seg.nii.gz\n    ├── image2_seg.nii.gz\n    ├── image3_seg.nii.gz\n    └── output.csv\n```\n\n### As a locally installed package\n\nA complete command would be (run from the directory of the package):\n\n```bash\nNiChart_Tissue_Segmentation -i /temp/in/ \\\n                            -o /temp/out/ \\\n                            -m /temp/nnUNet_model \\\n                            -c /temp/output.csv # Optional\n```\n\nFor further explanation please refer to the complete documentation:\n\n```bash\nNiChart_Tissue_Segmentation -h\n```\n\n### Using the docker container\n\nAn example command using the [docker container](https://hub.docker.com/repository/docker/aidinisg/nichart_tissue_segmentation/general) is the following:\n\n```bash\ndocker run -it --rm --gpus all -v ./:/workspace/ aidinisg/nichart_tissue_segmentation:0.0.0 NiChart_Tissue_Segmentation -i path/to/input -o path/to/output\n```\n\nPlease note that the model is provided in the docker container, but you can always substitute it with your own nnUNet model.\n\n### Example output\n\n```bash\ntemp\n├── in              \n│   ├── image1.nii.gz\n│   ├── image2.nii.gz\n│   └── image3.nii.gz\n├── nnUNet_model    \n│   └── nnUNet\n└── out                     // Output destination\n    ├── image1_seg.nii.gz\n    ├── image2_seg.nii.gz\n    ├── image3_seg.nii.gz\n    └── output.csv\n\n```\n\n## Contact\n\nFor more information, please contact [CBICA Software](mailto:software@cbica.upenn.edu).\n\n## For Developers\n\nContributions are welcome! Please refer to our [CONTRIBUTING.md](CONTRIBUTING.md) for more information on how to report bugs, suggest enhancements, and contribute code.\n\nIf you're a developer looking to contribute, you'll first need to set up a development environment. After cloning the repository, you can install the development dependencies with:\n\n```bash\npip install -r requirements-test.txt\n```\n\nThis will install the packages required for running tests and formatting code. Please make sure to write tests for new code and run them before submitting a pull request.\n\n© 2024 NiChart Team. All Rights Reserved.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbica%2Fnichart_tissue_segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcbica%2Fnichart_tissue_segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbica%2Fnichart_tissue_segmentation/lists"}