An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks

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  • License: GNU General Public License v3 (GPLv3) (GPLv3)
  • Author: Lalith Kumar Shiyam Sundar | Sebastian Gutschmayer | Manuel Pires
  • Tags 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
  • Requires: Python >=3.10
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Project 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.

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Maintainers
Avatar for lalithshiyam from gravatar.com lalithshiyam Avatar for mpires from gravatar.com mpires

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU General Public License v3 (GPLv3) (GPLv3)
  • Author: Lalith Kumar Shiyam Sundar | Sebastian Gutschmayer | Manuel Pires
  • Tags 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
  • Requires: Python >=3.10
Classifiers

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This version

3.0.4

3.0.3

3.0.2

3.0.1

3.0.0

2.6.4

2.6.3

2.6.2

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2.0.0

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