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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Verified details

These details have been verified by PyPI
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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3.0.4

3.0.3

3.0.2

This version

3.0.1

3.0.0

2.6.4

2.6.3

2.6.2

2.6.1

2.6.0

2.4.11

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2.0.0

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