  Head and neck (H&N) cancers are among the most prevalent types of cancer
worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management.
Recently, the diffusion model has demonstrated remarkable performance in
various image-generation tasks. In this work, we proposed a 3D diffusion model
to accurately perform H&N tumor segmentation from 3D PET and CT volumes. The 3D
diffusion model was developed considering the 3D nature of PET and CT images
acquired. During the reverse process, the model utilized a 3D UNet structure
and took the concatenation of PET, CT, and Gaussian noise volumes as the
network input to generate the tumor mask. Experiments based on the HECKTOR
challenge dataset were conducted to evaluate the effectiveness of the proposed
diffusion model. Several state-of-the-art techniques based on U-Net and
Transformer structures were adopted as the reference methods. Benefits of
employing both PET and CT as the network input as well as further extending the
diffusion model from 2D to 3D were investigated based on various quantitative
metrics and the uncertainty maps generated. Results showed that the proposed 3D
diffusion model could generate more accurate segmentation results compared with
other methods. Compared to the diffusion model in 2D format, the proposed 3D
model yielded superior results. Our experiments also highlighted the advantage
of utilizing dual-modality PET and CT data over only single-modality data for
H&N tumor segmentation.
