Welcome to LongiSeg!

What is LongiSeg?

LongiSeg is an extension of the popular nnU-Net framework, designed specifically for longitudinal medical image segmentation. By incorporating temporal information across multiple timepoints, LongiSeg enhances segmentation accuracy and consistency, making it a robust tool for analyzing medical imaging over time.

LongiSeg includes several methods for temporal feature merging, including the newly introduced Difference Weighting Block.    
For more details on the underlying nnU-Net framework, visit the nnU-Net repository.

Please cite the following works when using LongiSeg in your research:

@article{rokuss2024longitudinal,
  title={Longitudinal segmentation of MS lesions via temporal Difference Weighting},
  author={Rokuss, Maximilian and Kirchhoff, Yannick and Roy, Saikat and Kovacs, Balint and Ulrich, Constantin and Wald, Tassilo and Zenk, Maximilian and Denner, Stefan and Isensee, Fabian and Vollmuth, Philipp and Kleesiek, Jens and Maier-Hein, Klaus},
  journal={arXiv preprint arXiv:2409.13416},
  year={2024}
}
@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

Getting started

LongiSeg is easy to use and follows the basic principles of the nnU-Net framework.

In order to get started, we recommend to create a virtual environment with at least Python 3.10, e.g. using conda

conda create -n longiseg python=3.12

LongiSeg is not yet available via pip, therefore you need to clone the repository and install it locally. This will also allow for easy customization of the code.

conda activate longiseg
git clone https://github.com/MIC-DKFZ/LongiSeg.git
cd LongiSeg
pip install -e .

Finally, you need to set the paths for raw data, preprocessed data and results

export LongiSeg_raw="/path_to_data_dir/LongiSeg_raw"
export LongiSeg_preprocessed="/path_to_data_dir/LongiSeg_preprocessed"
export LongiSeg_results="/path_to_experiments_dir/LongiSeg_results"

If these are not set, LongiSeg will fall back to the respective nnU-Net paths, ensuring compatability with nnU-Net setups.

For more details on installation requirements and dataset structure, refer to the nnU-Net installation and the path setup documentation.

Using LongiSeg

Detailed usage instructions for LongiSeg can be found in the documentation.

TL;DR:

1. Prepare your dataset, ensuring it includes a patientsTr.json file.
2. Run experiment planning and preprocessing: LongiSeg_plan_and_preprocess -d DATASET_ID.
3. Train your model: LongiSeg_train DATASET_NAME_OR_ID UNET_CONFIGURATION FOLD
4. Run inference on unseen data: LongiSeg_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -path /path/to/patients.json -d DATASET_ID
5. Run evaluation on the predicted segmentation: LongiSeg_evaluate_folder GT_FOLDER PRED_FOLDER -djfile /path/to/dataset.json -pfile /path/to/plans.json -patfile /path/to/patients.json

Compatibility with nnU-Net

LongiSeg is fully compatible with nnU-Net and can be installed alongside it in the same environment. This allows users to seamlessly reuse existing nnU-Net structures, datasets, and preprocessing pipelines without modification.

Also check out: LesionLocator – Zero-Shot Tumor Tracking & Segmentation

If you're working on lesion or tumor segmentation and tracking, make sure to also check out our LesionLocator framework, introduced at CVPR 2025:

🎯 LesionLocator is a powerful zero-shot framework for segmentation and longitudinal tracking of lesions in 3D whole-body imaging — no lesion-specific training required. It supports prompt-based segmentation (e.g., 3D point or box prompts) and autoregressive tracking across timepoints.

👉 GitHub: https://github.com/MIC-DKFZ/LesionLocator

LesionLocator and LongiSeg share a focus on longitudinal analysis, but with different strengths:

• LongiSeg is ideal for fully supervised, dataset-specific training with temporal modeling across timepoints.
• LesionLocator excels at zero-shot generalization and supports interactive & promptable workflows out of the box.

Use them together to benchmark traditional vs. zero-shot approaches — or combine insights from both for even better longitudinal segmentation performance.

Acknowledgements

LongiSeg is developed and maintained by the Division of Medical Image Computing at the German Cancer Research Center (DKFZ), supported by the Helmholtz Association under the joint research school “HIDSS4Health – Helmholtz Information and Data Science School for Health.

nnU-Net is developed and maintained by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at the German Cancer Research Center (DKFZ).