moosez

Name	moosez JSON
Version	3.0.10 JSON
	download
home_page	https://github.com/ENHANCE-PET/MOOSE
Summary	An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks
upload_time	2025-01-10 21:22:55
maintainer	None
docs_url	None
author	Lalith Kumar Shiyam Sundar | Sebastian Gutschmayer | Manuel Pires
requires_python	>=3.9
license	GPLv3
keywords	moosez model-zoo nnunet medical-imaging tumor-segmentation organ-segmentation bone-segmentation lung-segmentation muscle-segmentation fat-segmentation vessel-segmentation vertebral-segmentation rib-segmentation preclinical-segmentation clinical-segmentation
VCS
bugtrack_url
requirements	nnunetv2 halo SimpleITK pydicom argparse numpy openpyxl pyfiglet natsort colorama dask rich pandas dicom2nifti emoji requests psutil scipy nibabel
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            mooseZ is an AI-inference engine based on nnUNet, designed for 3D clinical and preclinical whole-body segmentation tasks. It serves models tailored towards different modalities such as PET, CT, and MR. mooseZ provides fast and accurate segmentation results, making it a reliable tool for medical imaging applications.

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/ENHANCE-PET/MOOSE",
    "name": "moosez",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.9",
    "maintainer_email": null,
    "keywords": "moosez model-zoo nnUNet medical-imaging tumor-segmentation organ-segmentation bone-segmentation lung-segmentation muscle-segmentation fat-segmentation vessel-segmentation vertebral-segmentation rib-segmentation preclinical-segmentation clinical-segmentation",
    "author": "Lalith Kumar Shiyam Sundar | Sebastian Gutschmayer | Manuel Pires",
    "author_email": "Lalith.shiyamsundar@meduniwien.ac.at",
    "download_url": "https://files.pythonhosted.org/packages/4c/45/ce226a0c3a97c533890420fe2b312d80cc12027b6f57896cf33130056bdd/moosez-3.0.10.tar.gz",
    "platform": null,
    "description": "mooseZ is an AI-inference engine based on nnUNet, designed for 3D clinical and preclinical whole-body segmentation tasks. It serves models tailored towards different modalities such as PET, CT, and MR. mooseZ provides fast and accurate segmentation results, making it a reliable tool for medical imaging applications.\n",
    "bugtrack_url": null,
    "license": "GPLv3",
    "summary": "An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks",
    "version": "3.0.10",
    "project_urls": {
        "Homepage": "https://github.com/ENHANCE-PET/MOOSE"
    },
    "split_keywords": [
        "moosez",
        "model-zoo",
        "nnunet",
        "medical-imaging",
        "tumor-segmentation",
        "organ-segmentation",
        "bone-segmentation",
        "lung-segmentation",
        "muscle-segmentation",
        "fat-segmentation",
        "vessel-segmentation",
        "vertebral-segmentation",
        "rib-segmentation",
        "preclinical-segmentation",
        "clinical-segmentation"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "3a48cfadd563937cfccd365cd8b5d83cbee3f645ed67d024a1ce9240605391d8",
                "md5": "d5c2c11127e226819523bb0de12e533a",
                "sha256": "c15e8ee60e4d4dec05f5a0dad83b51dc44dd7d9c8257b47a4cc2b4366273d58b"
            },
            "downloads": -1,
            "filename": "moosez-3.0.10-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "d5c2c11127e226819523bb0de12e533a",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.9",
            "size": 40837,
            "upload_time": "2025-01-10T21:22:53",
            "upload_time_iso_8601": "2025-01-10T21:22:53.337619Z",
            "url": "https://files.pythonhosted.org/packages/3a/48/cfadd563937cfccd365cd8b5d83cbee3f645ed67d024a1ce9240605391d8/moosez-3.0.10-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "4c45ce226a0c3a97c533890420fe2b312d80cc12027b6f57896cf33130056bdd",
                "md5": "b82318ad757bc93cc1c76e8e25655e38",
                "sha256": "67040249d349701d25408c0ffa936b42a3bdb7c5a83a2166862b893fc7da3a51"
            },
            "downloads": -1,
            "filename": "moosez-3.0.10.tar.gz",
            "has_sig": false,
            "md5_digest": "b82318ad757bc93cc1c76e8e25655e38",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.9",
            "size": 43636,
            "upload_time": "2025-01-10T21:22:55",
            "upload_time_iso_8601": "2025-01-10T21:22:55.664047Z",
            "url": "https://files.pythonhosted.org/packages/4c/45/ce226a0c3a97c533890420fe2b312d80cc12027b6f57896cf33130056bdd/moosez-3.0.10.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-01-10 21:22:55",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "ENHANCE-PET",
    "github_project": "MOOSE",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "requirements": [
        {
            "name": "nnunetv2",
            "specs": []
        },
        {
            "name": "halo",
            "specs": [
                [
                    "~=",
                    "0.0.31"
                ]
            ]
        },
        {
            "name": "SimpleITK",
            "specs": [
                [
                    "~=",
                    "2.2.1"
                ]
            ]
        },
        {
            "name": "pydicom",
            "specs": [
                [
                    "~=",
                    "2.2.2"
                ]
            ]
        },
        {
            "name": "argparse",
            "specs": [
                [
                    "~=",
                    "1.4.0"
                ]
            ]
        },
        {
            "name": "numpy",
            "specs": []
        },
        {
            "name": "openpyxl",
            "specs": [
                [
                    "~=",
                    "3.0.9"
                ]
            ]
        },
        {
            "name": "pyfiglet",
            "specs": [
                [
                    "~=",
                    "0.8.post1"
                ]
            ]
        },
        {
            "name": "natsort",
            "specs": [
                [
                    "~=",
                    "8.1.0"
                ]
            ]
        },
        {
            "name": "colorama",
            "specs": [
                [
                    "~=",
                    "0.4.6"
                ]
            ]
        },
        {
            "name": "dask",
            "specs": [
                [
                    "~=",
                    "2023.6.0"
                ]
            ]
        },
        {
            "name": "rich",
            "specs": []
        },
        {
            "name": "pandas",
            "specs": []
        },
        {
            "name": "dicom2nifti",
            "specs": [
                [
                    "~=",
                    "2.4.8"
                ]
            ]
        },
        {
            "name": "emoji",
            "specs": []
        },
        {
            "name": "requests",
            "specs": []
        },
        {
            "name": "psutil",
            "specs": []
        },
        {
            "name": "scipy",
            "specs": []
        },
        {
            "name": "nibabel",
            "specs": []
        }
    ],
    "lcname": "moosez"
}