Metadata-Version: 2.4
Name: moosez
Version: 3.0.30
Summary: An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks
Home-page: https://github.com/ENHANCE-PET/MOOSE
Author: Lalith Kumar Shiyam Sundar | Sebastian Gutschmayer | Manuel Pires
Author-email: Lalith.shiyamsundar@meduniwien.ac.at
License: Apache-2.0
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
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Healthcare Industry
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.10
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: SimpleITK
Requires-Dist: nnunetv2>=2.6.0
Requires-Dist: halo
Requires-Dist: pydicom
Requires-Dist: argparse
Requires-Dist: numpy<2.0
Requires-Dist: pyfiglet
Requires-Dist: natsort
Requires-Dist: colorama
Requires-Dist: dask
Requires-Dist: rich
Requires-Dist: pandas
Requires-Dist: dicom2nifti
Requires-Dist: emoji
Requires-Dist: matplotlib
Requires-Dist: psutil
Requires-Dist: nibabel
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

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.
