LongiTrack

LongiTrack provides point-prompted longitudinal lesion tracking and segmentation for CT. It runs through LongiSeg using CT images and a tracking.json file.

Paper: Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking

Research use only. LongiTrack is not a medical device and must not be used as the sole basis for diagnosis, treatment, or other clinical decisions.

Download and install

Install Git LFS and uv first.

git lfs install
git clone https://huggingface.co/ykirchhoff/LongiTrack
cd LongiTrack
uv sync

Run tracking inference

tracking.json must follow the LongiSeg tracking-file format.

uv run longitrack-predict \
  --images-path /path/to/images \
  --output-path /path/to/output \
  --tracking-path /path/to/tracking.json

Supported options:

Option	Default	Description
--images-path	—	Input image directory.
--output-path	—	Output directory.
--tracking-path	—	Path to tracking.json.
--version	1.1	Model version to use for inference.
--mode	automatic	Tracking mode: automatic or manual.
--device	cuda	Inference device: cuda, cpu, or mps.

The selected version determines the model directory. Available checkpoint folds are detected automatically.

Versions

Version	Model directory	Training data
1.0	LongiTrack_v1.0/	AutoPET IV
1.1	LongiTrack_v1.1/	AutoPET IV, PanTrack, HCUCH

Interactive viewer

LongiTrack-napari provides the interactive napari workflow for reviewing and correcting tracking points.

Citation

LongiTrack and LongiSeg

@inproceedings{kirchhoff2026exploiting,
  title     = {Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking},
  author    = {Kirchhoff, Yannick and Rokuss, Maximilian and Mertens, Daniel Philipp and F{\"u}ller, David and Hamm, Benjamin and Schreyer, Andreas and Ritter, Oliver and Maier-Hein, Klaus},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
  year      = {2026}
}

@inproceedings{rokuss2024longitudinal,
  title     = {Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting},
  author    = {Rokuss, Maximilian R 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},
  booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages     = {64--74},
  year      = {2024},
  organization = {Springer}
}

Data

@misc{kuestner2025longitudinal,
  author = {K\"ustner, Thomas and Peisen, Felix and Gatidis, Sergios and Wagner, Andreas and Megne, Ornela and Othman, Ahmed and Sanner, Antoine and Lo\ssau, Tanja and Moltz, Jan Hendrik and Kohlbrandt, Temke and Hering, Alessa},
  title = {Longitudinal-CT},
  year = {2025},
  publisher = {University of T\"ubingen},
  doi = {10.57754/FDAT.qwsry-7t837}
}

@article{rojas2026ct,
  title     = {A CT Dataset with RECIST Measurements and Comprehensive Segmentation Masks for Tumors and Lymph Nodes},
  author    = {Rojas-Pizarro, Roberto and V{\'a}squez-Venegas, Constanza and Pereira, Gonzalo and Eyssautier, Mar{\'\i}a F and Bravo-Baham{\'o}ndez, Felipe and Sanhueza, Nicol{\'a}s and Gallardo-Badilla, Paulina and Caro-Flores, Francisca and Orme{\~n}o-Candia, Camila and Santander, Felipe and others},
  journal   = {Scientific Data},
  volume    = {13},
  number    = {1},
  pages     = {270},
  year      = {2026},
  publisher = {Nature Publishing Group UK London}
}