moosez

Name	moosez JSON
Version	3.0.23 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	2025-07-23 11:22:25
maintainer	None
docs_url	None
author	Lalith Kumar Shiyam Sundar | Sebastian Gutschmayer | Manuel Pires
requires_python	>=3.10
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 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.

            

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