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

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

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View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

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License: GNU General Public License v3 (GPLv3) (GPLv3)

Author: Lalith Kumar Shiyam Sundar | Sebastian Gutschmayer

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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2.4.7

2.4.6

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