HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Dataset

Creators

• Gašper Podobnik
• Primož Strojan
• Primož Peterlin
• Bulat Ibragimov
• Tomaž Vrtovec

Description

The HaN-Seg: Head and Neck Organ-at-Risk CT & MR Segmentation Dataset is a publicly available dataset of anonymized head and neck (HaN) images of 42 patients that underwent both CT and T1-weighted MR imaging for the purpose of image-guided radiotherapy planning.

In addition, the dataset also contains reference segmentations of 30 organs-at-risk (OARs) for CT images in the form of binary segmentation masks, which were obtained by curating manual pixel-wise expert image annotations.

A full description of the HaN-Seg dataset can be found in:
G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset", Medical Physics, 2023.

Any research originating from its usage is required to cite this paper.

Challenge

In parallel with the release of the dataset, the HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched to promote the development of new and application of existing state-of-the-art fully automated techniques for OAR segmentation in the HaN region from CT images that exploit the information of multiple imaging modalities, in this case from CT and MR images.

The task of the HaN-Seg challenge is to automatically segment up to 30 OARs in the HaN region from CT images in the devised test set, consisting of 14 CT and MR images of the same patients, given the availability of the training set (i.e., the herein publicly available HaN-Seg dataset), consisting of 42 CT and MR images of the same patients with reference 3D OAR binary segmentation masks for CT images.

More

Please find below a list of relevant publications that address: (1) the assessment of inter-observer and inter-modality variability in OAR contouring, (2) results of the HaN-Seg challenge, (3) development of our multimodal segmentation model, and (4) development of MR-to-CT image-to-image translation using diffusion models:

• 1. G. Podobnik, B. Ibragimov, P. Strojan, P. Peterlin, T. Vrtovec, "vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images", Medical Physics, 2024. Click here.
• 2. G. Podobnik, B. Ibragimov, E. Tappeiner, et al., "HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge", Radiotherapy and Oncology, 2024. Click here.
• 3. G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "Multimodal CT and MR Segmentation of Head and Neck Organs-at-Risk", MICCAI 2023, 2023. Click here.
• 4. R. M. Šter, G. Podobnik, T. Vrtovec, "Diffusion-based MR-to-CT translation of head and neck images", SPIE Medical Imaging 2025, 2025. Click here.

Notes

If you are using the HaN-Seg public training dataset, you are required to cite the following article:

G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset", Medical Physics, 2023.

@ARTICLE{HaNSeg_dataset,
  author = {Gašper Podobnik, Primož Strojan, Primož Peterlin, Bulat Ibragimov, Tomaž Vrtovec},
  title = {{HaN-Seg}: {T}he head and neck organ-at-risk {CT} \& {MR} segmentation dataset},
  journal = {Medical Physics},
  year = {2023},
  doi = {10.1002/mp.16197}
}