Head and Neck Oropharyngeal GTV Autosegmentation: Combining Nnu-Net with Shape Representation Loss Driven by a Variational Autoencoder Model.

These results show that nnU-Net can be used to generate a well-performing autosegmentation model for head and neck GTV segmentation using multimodal input. We also demonstrate that the PyTorch framework can be readily altered and optimized for particular tasks. Our shape representation loss was able to improve performance and achieve the top segmentation result in the HECKTOR challenge. Further optimizations into this framework are actively being explored.