Metadata-Version: 2.4
Name: nagini3D
Version: 0.0.2
Summary: Implementation of NAGINI-3D, a python package designed for Multi-Object Segmentation in Biological Imaging based on deep learning and active surfaces.
Project-URL: Homepage, https://github.com/QuentinRapilly/NAGINI-3D
Project-URL: Issues, https://github.com/QuentinRapilly/NAGINI-3D/issues
Author-email: Quentin RAPILLY <quentin.RAPILLY@inria.fr>
License-Expression: AGPL-3.0
License-File: LICENSE
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Requires-Dist: csbdeep==0.7.4
Requires-Dist: einops==0.7.0
Requires-Dist: omegaconf==2.3.0
Requires-Dist: scikit-image==0.21.0
Requires-Dist: scipy==1.11.3
Provides-Extra: full
Requires-Dist: hydra-core==1.3.2; extra == 'full'
Requires-Dist: numpy==1.26.4; extra == 'full'
Requires-Dist: tifffile==2024.2.12; extra == 'full'
Requires-Dist: wandb==0.15.12; extra == 'full'
Description-Content-Type: text/markdown

# NAGINI-3D | Prediction of Parametric Surfaces for Multi-Object Segmentation in 3D Biological Imaging

We present NAGINI-3D (N-Active shapes for seGmentINg 3D biological Images), a method dedicated to 3D biological images segmentation, based on both deep learning (CNN) and Active Surfaces (Snakes).

This package implement the method described in:

- Quentin RAPILLY, Pierre MAINDRON, Guenaelle BOUET-CHALON, Anaïs BADOUAL, Charles KERVRANN.
[*Prediction of Parametric Surfaces for Multi-Object Segmentation in 3D Biological Imaging*](https://hal.science/hal-04978619). International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), Totnes, England, May 2025.

All details on the implementation and the tutorials are available on [the github of the project.](https://github.com/QuentinRapilly/NAGINI-3D)
