Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know.

The proposed approach extracts knowledge of experts performing IGRT corrections to enable new rigid registration methods that are capable of mimicking human decisions. In the future, the deduction of knowledge-based corrections for different cohorts can be established automating such supervised learning approaches.