nnUNet-package 0.3.5

Package to use nnUNet on 3D Slicer

LungSegmentation nnUNetv2 Prediction Script

A Python script to easily run nnUNetv2 lung segmentation predictions on medical images. Automatic model download, file preparation for prediction, and result renaming included.

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Features

• Automatic download and extraction of models from a URL.
• Preparation of dataset.json for nnUNet prediction.
• Conversion of input images to .nrrd if necessary.
• Prediction execution with detailed logs.
• Automatic cleanup of temporary files.
• Automatic renaming of the final prediction file.

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Requirements

Before running the script, make sure you have installed and configured the following:

git clone https://github.com/FlorianDAVAUX/nnUNet_package.git
cd nnUNet_package
pip install -e .

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Usage

Option	Description	Example
--mode	Prediction mode (Invivo or Exvivo)	--mode Invivo
--structure	Structure to segment (Parenchyma, Airways, Vascular, ParenchymaAirways, All, Lobes)	--structure Parenchyma
--input	Path to the input image (.nii, .nii.gz, .mha, .nrrd)	--input ~/data/scan_patient.nrrd
--output	Output directory for the prediction (default: prediction)	--output ~/predictions
--models_dir	Path to store or search for models	--models_dir ~/models
--name	Final name of the prediction file (without extension)	--name segmentation_parenchyma

Full Example

nnunet_predict --mode Invivo --structure Parenchyma --input ~/data/scan_patient.nrrd --output ~/predictions --models_dir ~/models --name segmentation_parenchyma