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70 lines (62 loc) · 1.83 KB
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[build-system]
requires = ["setuptools>=64", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "moosez"
version = "3.2.2"
description = "An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks"
readme = "README.md"
requires-python = ">=3.10"
license = { text = "Apache-2.0" }
authors = [
{ name = "Lalith Kumar Shiyam Sundar", email = "Lalith.shiyam@med.uni-muenchen.de" },
{ name = "Sebastian Gutschmayer", email = "sebastian.gutschmayer@meduniwien.ac.at" },
{ name = "Manuel Pires", email = "manuel.pires@meduniwien.ac.at" },
]
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",
]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Healthcare Industry",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering :: Medical Science Apps.",
]
dependencies = [
"torch",
"SimpleITK",
"nnunetv2>=2.6.0",
"halo",
"pydicom>=2.4.5",
"numpy",
"pyfiglet",
"natsort",
"colorama",
"dask>=2026.1.0",
"rich",
"pandas",
"dicom2nifti",
'dcm2niix',
"emoji",
"matplotlib",
"psutil",
"requests>=2.33.0",
"nibabel"
]
[project.scripts]
moosez = "moosez.moosez:main"
[tool.setuptools.packages.find]
where = ["."]
include = ["moosez*"]
[tool.setuptools.package-data]
"moosez.mappings" = ["*.csv"]