  The treatment planning process for patients with head and neck (H&N) cancer
is regarded as one of the most complicated due to large target volume, multiple
prescription dose levels, and many radiation-sensitive critical structures near
the target. Treatment planning for this site requires a high level of human
expertise and a tremendous amount of effort to produce personalized high
quality plans, taking as long as a week, which deteriorates the chances of
tumor control and patient survival. To solve this problem, we propose to
investigate a deep learning-based dose prediction model, Hierarchically Densely
Connected U-net, based on two highly popular network architectures: U-net and
DenseNet. We find that this new architecture is able to accurately and
efficiently predict the dose distribution, outperforming the other two models,
the Standard U-net and DenseNet, in homogeneity, dose conformity, and dose
coverage on the test data. Averaging across all organs at risk, our proposed
model is capable of predicting the organ-at-risk max dose within 6.3% and mean
dose within 5.1% of the prescription dose on the test data. The other models,
the Standard U-net and DenseNet, performed worse, having an averaged
organ-at-risk max dose prediction error of 8.2% and 9.3%, respectively, and
averaged mean dose prediction error of 6.4% and 6.8%, respectively. In
addition, our proposed model used 12 times less trainable parameters than the
Standard U-net, and predicted the patient dose 4 times faster than DenseNet.
