  Background and purpose: Deep Learning (DL) has been widely explored for
Organs at Risk (OARs) segmentation; however, most studies have focused on a
single modality, either CT or MRI, not both simultaneously. This study presents
a high-performing DL pipeline for segmentation of 30 OARs from MRI and CT scans
of Head and Neck (H&N) cancer patients.
  Materials and methods: Paired CT and MRI-T1 images from 42 H&N cancer
patients alongside annotation for 30 OARs from the H&N OAR CT & MR segmentation
challenge dataset were used to develop a segmentation pipeline. After cropping
irrelevant regions, rigid followed by non-rigid registration of CT and MRI
volumes was performed. Two versions of the CT volume, representing soft tissues
and bone anatomy, were stacked with the MRI volume and used as input to an
nnU-Net pipeline. Modality Dropout was used during the training to force the
model to learn from the different modalities. Segmentation masks were predicted
with the trained model for an independent set of 14 new patients. The mean Dice
Score (DS) and Hausdorff Distance (HD) were calculated for each OAR across
these patients to evaluate the pipeline.
  Results: This resulted in an overall mean DS and HD of 0.777 +- 0.118 and
3.455 +- 1.679, respectively, establishing the state-of-the-art (SOTA) for this
challenge at the time of submission.
  Conclusion: The proposed pipeline achieved the best DS and HD among all
participants of the H&N OAR CT and MR segmentation challenge and sets a new
SOTA for automated segmentation of H&N OARs.
