Metadata-Version: 2.1
Name: moosez
Version: 2.0.0
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
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
Platform: UNKNOWN
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.
License-File: LICENSE

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.

