  Segmentation of head and neck (H\&N) tumours and prediction of patient
outcome are crucial for patient's disease diagnosis and treatment monitoring.
Current developments of robust deep learning models are hindered by the lack of
large multi-centre, multi-modal data with quality annotations. The MICCAI 2021
HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge
creates a platform for comparing segmentation methods of the primary gross
target volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography images
and prediction of progression-free survival in H\&N oropharyngeal cancer.For
the segmentation task, we proposed a new network based on an encoder-decoder
architecture with full inter- and intra-skip connections to take advantage of
low-level and high-level semantics at full scales. Additionally, we used
Conditional Random Fields as a post-processing step to refine the predicted
segmentation maps. We trained multiple neural networks for tumor volume
segmentation, and these segmentations were ensembled achieving an average Dice
Similarity Coefficient of 0.75 in cross-validation, and 0.76 on the challenge
testing data set. For prediction of patient progression free survival task, we
propose a Cox proportional hazard regression combining clinical, radiomic, and
deep learning features. Our survival prediction model achieved a concordance
index of 0.82 in cross-validation, and 0.62 on the challenge testing data set.
