LyROI – nnU-Net-based Lymphoma Total Metabolic Tumor Volume Delineation

Overview

Lymphoma ROI prediction framework (LyROI) is a collection of neural network models and support tools for metabolic tumor volume delineation in (Non-Hodgkin) lymphoma patients in FDG-PET/CT images.

A comprehensive description of development and evaluation of the models is given in the respective paper. Briefly, the models were trained with the nnU-Net software package. A total of 1192 FDG-PET/CT scans from 716 patients with Non-Hodgkin lymphoma participating in the PETAL trial comprised the training dataset. The ground truth delineation included all lesions (irrespective of size or uptake) that were clinically considered as lymphoma manifestations by an experienced observer. It was developed iteratively with the assistance of intermediate CNN models. Accurate contouring of each lesion was achieved by selecting the most appropriate semi-automated delineation algorithm, manually adjusting its settings, and performing manual corrections when necessary.

Training and testing were performed following a 5-fold cross-validation scheme. Three configurations of the nnU-Net were used for training: regular U-Net, residual encoder U-Net (8 GB GPU memory target), and large residual encoder U-Net (24 GB GPU memory target). They can be installed as described below and used separately, however, their use in an ensemble (merging individual outputs via union operation) is recommended to maximize lesion detection sensitivity.

The collection of the trained models can be found here:

List of available models:

• PET/CT:
  • LyROI_Orig.zip: regular U-Net
  • LyROI_ResM.zip: residual encoder U-Net (medium)
  • LyROI_ResL.zip: residual encoder U-Net (large)
• PET-only:
  • LyROI_PET_Orig.zip: regular U-Net
  • LyROI_PET_ResM.zip: residual encoder U-Net (medium)
  • LyROI_PET_ResL.zip: residual encoder U-Net (large)

Scripts subfolder provides example code snippets to execute the prediction with each model and merge the resulting delineations when using directly within existing nnU-Net installation. See manual installation section for more details. For simplified workflow, LyROI is also available as a stand-alone tool, see quick start section for usage and installation instructions.

Please cite nnU-Net and the following paper when using LyROI:

Nikulin, P., Hoberück, S., Apostolova, I. et al. A convolutional neural network for fully automated total metabolic tumor volume delineation in patients with aggressive Non-Hodgkin lymphoma. Eur J Nucl Med Mol Imaging (2026). Advance online publication. https://doi.org/10.1007/s00259-026-07810-9

Special thanks to the PETAL study group for the access to the PETAL trial dataset for the network training and agreeing to share the resulting models.

Quick Start

Requirements

• python (>= 3.9)
• pytorch (>= 2.1.2)

1. Install python and pytorch (see requirements list above).

   Note: As of now, torch >=2.9.0 may lead to severe performance reduction. Earlier versions are recommended.

2. Install lyroi as a package (recommended):

   pip install lyroi
   

   or as modifiable copy (for experienced users):

   git clone https://github.com/hzdr-MedImaging/LyROI.git
   cd LyROI
   pip install -e .
   

3. (OPTIONAL) To change the default model installation directory (default: $HOME/.lyroi), set the environment variable LYROI_DIR to the desired location. See, e.g. here for guidance.

4. Download and install the model files:

   lyroi_install
   

5. Run LyROI for

   • all images in the input_folder (see below for input data format) and output delineation in output_folder:

     lyroi -i input_folder -o output_folder
     

   • a single patient with CT image ct.nii.gz and PET image pet.nii.gz (must be coregistered and have the same matrix and voxel sizes) and output delineation to roi.nii.gz:

     lyroi -i ct.nii.gz pet.nii.gz -o roi.nii.gz
     

   Execution on a GPU-equipped workstation is highly recommended. In case if no GPU is available, use a flag -d cpu to force run on CPU (can be VERY slow). Flag -d cpu-max can help with cpu performance by using all available computational resources (may slow down other programs). nnUNet_def_n_proc environment variable can be set to limit the number of utilized cpu cores in cpu-max mode.

6. (ALTERNATIVE) Use LyROI via graphical user interface:

   lyroi_gui
   

   You can also create a shortcut for lyroi_gui executable which can make your life easier if you are not comfortable using command line. To find the executable, run

   • Linux:

     which lyroi_gui
     

   • Windows:

     where lyroi_gui
     

Manual Installation and Use

Requirements

• python (>= 3.9)
• pytorch (>= 2.1.2)
• nnU-Net (>= 2.5.2)

1. To download and install the models for each used nnU-Net configuration, execute:

   nnUNetv2_install_pretrained_model_from_zip https://rodare.hzdr.de/record/4177/files/LyROI_Orig.zip
   nnUNetv2_install_pretrained_model_from_zip https://rodare.hzdr.de/record/4177/files/LyROI_ResM.zip
   nnUNetv2_install_pretrained_model_from_zip https://rodare.hzdr.de/record/4177/files/LyROI_ResL.zip
   

2. By default, the models will be installed in the folder $nnUNet_results/Dataset001_LyROI/. This might create conflicts if you already have a project with the number 001 in your $nnUNet_results folder. In this case, please choose an unoccupied index XXX for the dataset and rename the LyROI folder to DatasetXXX_LyROI.
3. Download all files in scripts folder and put them in the same folder. If you changed the dataset index of LyROI, edit the predict.sh file and change the dataset_id="001" line to dataset_id="XXX", where XXX is the new dataset index you selected.
4. Prepare the input data according to the instructions below.
5. Execute ./predict.sh /path/to/your/folder/input_folder and wait for the process to complete. The resulting delineations can be found in input_folder/pred/ subfolder. If you want to keep the outputs of the intermediate networks, comment out the last line in predict.sh. Execution on a GPU-equipped workstation is highly recommended. In case if no GPU is available, add a flag -device cpu to nnUNetv2_predict calls within the script (can be VERY slow). Set nnUNet_def_n_proc environment variable to specify the number of cpu cores to use for inference (set to the number of physical cpu cores for max performance).

Data Format

The input data for batch processing should be presented in the nnU-Net compatible format (see here for details). Only compressed NIfTI (.nii.gz) images are currently supported. Corresponding CT and PET volumes must be coregistered and have the same matrix and voxel sizes.

Input channels:

• 0000 is CT
• 0001 is PET

Here is an example of how the input folder can look like:

input_folder
      ├── lymph_20250101_0000.nii.gz
      ├── lymph_20250101_0001.nii.gz
      ├── pat01_0000.nii.gz
      ├── pat01_0001.nii.gz
      ├── rchop001_0000.nii.gz
      ├── rchop001_0001.nii.gz
      ├── ...

Intended Purpose (Non-Medical)

• The software is intended for algorithmic research, benchmarking, and method exploration in lymphoma delineation.
• It is not intended to provide information for diagnostic or therapeutic purposes and must not be used in clinical workflows.
• Do not deploy or advertise this software as a medical product or service.

Disclaimer (Research Use Only – Not a Medical Device)

This software and any bundled or referenced model weights are provided exclusively for research and development purposes. They are not intended for use in the diagnosis, cure, mitigation, treatment, or prevention of disease, or for any other clinical decision-making.

• The software is not a medical device and is not CE-marked.
• No clinical performance, safety, or effectiveness is claimed or implied.
• Any results must not be used to guide patient management.
• Users are responsible for compliance with all applicable laws, regulations, and data protection requirements when processing data.

THE SOFTWARE AND MODELS ARE PROVIDED “AS IS”, WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED.

Licenses

The code in this repository is licensed under Apache-2.0 (see LICENSE).
The model weights are licensed under CC-BY-SA-4.0 (see MODEL_LICENSE.md).

Third-Party Licenses

This project uses or interoperates with the following third-party components:

• nnU-Net v2 – Copyright © respective authors.
  • License: Apache-2.0
• PyTorch, NumPy, Nibabel, etc.
  • Licensed under their respective open-source licenses.

Each third-party component is the property of its respective owners and is provided under its own license terms. Copies of these licenses are available from the upstream projects.