For the head and neck (HaN) cancer, radiotherapy is a mainstay treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional segmentation of OARs from computed tomography (CT) images is required for optimal radiation dose distribution calculation, however, so far there has been no evaluation about the impact of the combined analysis of multiple imaging modalities, such as CT and magnetic resonance (MR). For this purpose, we have devised a database of 56 CT and MR images of the same patients with 31 manually delineated OARs, and in this paper we present the baseline segmentation results that were obtained by applying the nnU-Net framework. The resulting average Dice coefficient of 68% and average 95-percentile Hausdorff distance of 8.2mm on a subset of 14 images indicate that nnU-Net serves as a solid baseline method.