Metadata-Version: 2.1
Name: moosez
Version: 2.2.14
Summary: An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks
Home-page: https://github.com/QIMP-Team/mooseZ
Author: Lalith Kumar Shiyam Sundar | Sebastian Gutschmayer
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 :: Apache Software License
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.9
License-File: LICENSE
Requires-Dist: nnunetv2
Requires-Dist: nibabel (~=3.2.2)
Requires-Dist: halo (~=0.0.31)
Requires-Dist: pandas (~=1.4.1)
Requires-Dist: SimpleITK (~=2.2.1)
Requires-Dist: pydicom (~=2.2.2)
Requires-Dist: argparse (~=1.4.0)
Requires-Dist: imageio (~=2.16.1)
Requires-Dist: numpy
Requires-Dist: mpire (~=2.3.3)
Requires-Dist: openpyxl (~=3.0.9)
Requires-Dist: matplotlib
Requires-Dist: pyfiglet (~=0.8.post1)
Requires-Dist: natsort (~=8.1.0)
Requires-Dist: pillow (>=9.2.0)
Requires-Dist: colorama (~=0.4.6)
Requires-Dist: dask
Requires-Dist: rich
Requires-Dist: pandas
Requires-Dist: dicom2nifti (~=2.4.8)
Requires-Dist: emoji
Requires-Dist: dask[distributed]
Requires-Dist: opencv-python

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.
