  Automatic segmentation of head and neck tumors plays an important role in
radiomics analysis. In this short paper, we propose an automatic segmentation
method for head and neck tumors from PET and CT images based on the combination
of convolutional neural networks (CNNs) and hybrid active contours.
Specifically, we first introduce a multi-channel 3D U-Net to segment the tumor
with the concatenated PET and CT images. Then, we estimate the segmentation
uncertainty by model ensembles and define a segmentation quality score to
select the cases with high uncertainties. Finally, we develop a hybrid active
contour model to refine the high uncertainty cases. Our method ranked second
place in the MICCAI 2020 HECKTOR challenge with average Dice Similarity
Coefficient, precision, and recall of 0.752, 0.838, and 0.717, respectively.
