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

Navigation

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

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.

Project details

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

Release history Release notifications | RSS feed

3.0.4

3.0.3

3.0.2

3.0.1

This version

3.0.0

2.6.4

2.6.3

2.6.2

2.6.1

2.6.0

2.4.11

2.4.10

2.4.9

2.4.8

2.4.7

2.4.6

2.4.5

2.4.4

2.4.3

2.4.2

2.4.1

2.4.0

2.3.9

2.3.8

2.3.5

2.3.4

2.3.3

2.3.2

2.3.1

2.3.0

2.2.39

2.2.38

2.2.37

2.2.36

2.2.35

2.2.34

2.2.33

2.2.32

2.2.31

2.2.30

2.2.29

2.2.28

2.2.27

2.2.26

2.2.25

2.2.24

2.2.23

2.2.22

2.2.21

2.2.20

2.2.19

2.2.18

2.2.17

2.2.16

2.2.15

2.2.14

2.2.13

2.2.12

2.2.11

2.2.10

2.2.9

2.2.8

2.2.7

2.2.6

2.2.5

2.2.4

2.2.3

2.2.2

2.2.1

2.2.0

2.1.44

2.1.43

2.1.42

2.1.41

2.1.40

2.1.39

2.1.38

2.1.36

2.1.35

2.1.32

2.1.31

2.1.30

2.1.29

2.1.28

2.1.27

2.1.26

2.1.25

2.1.23

2.1.22

2.1.21

2.1.20

2.1.19

2.1.17

2.1.16

2.1.15

2.1.14

2.1.12

2.1.11

2.1.10

2.1.9

2.1.8

2.1.7

2.1.6

2.1.5

2.1.4

2.1.3

2.1.2

2.1.1

2.1.0

2.0.20

2.0.19

2.0.18

2.0.17

2.0.16

2.0.15

2.0.14

2.0.13

2.0.12

2.0.10

2.0.9

2.0.8

2.0.7

2.0.6

2.0.5

2.0.4

2.0.3

2.0.2

2.0.1

2.0.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

moosez-3.0.0.tar.gz (49.9 kB view details)

Uploaded Source

Built Distribution

moosez-3.0.0-py3-none-any.whl (47.3 kB view details)

Uploaded Python 3

File details

Details for the file moosez-3.0.0.tar.gz.

File metadata

  • Download URL: moosez-3.0.0.tar.gz
  • Upload date:
  • Size: 49.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for moosez-3.0.0.tar.gz
Algorithm Hash digest
SHA256 53603e7c2977cb546d74e9fc6ecd48417661fa759a42c2f883bc3476c7b1d392
MD5 8179837bcfc6b18ba492ef6c112e1cf3
BLAKE2b-256 614ffd2ba12090182881b621a00911f0c6c0dd9f61665eb101862478b276a265

See more details on using hashes here.

File details

Details for the file moosez-3.0.0-py3-none-any.whl.

File metadata

  • Download URL: moosez-3.0.0-py3-none-any.whl
  • Upload date:
  • Size: 47.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for moosez-3.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 00f91282139f831c14ab503000edbd72f6ff4282b3f1dfe51b807c7677efda6d
MD5 9ba3910b83cd62b2e95ebc1d9d35ab2b
BLAKE2b-256 740205e87a6910ec5f1fea34cb722aab87730122d49cc67bf02b620278ef6179

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page