Joint Learning of Image Registration and Change Detection for Lung CT Images

Intuitive visualization of relevant changes between radiological image
pairs in the form of change maps has the potential to not only increase efficiency
in diagnostic reading, but also to decrease the number of missed abnormalities.
Classically, change maps are created from difference images after an image regis-
tration step, which requires a careful balance in order to neither generate artifacts
nor disguise relevant changes. We propose jointly learning registration and change
map in order to address these limitations. As a proof of concept, the method was
tested on NLST lung CT images and synthetically generated data, and shows com-
parable results to the conventional approach. In a reader study, the use of change
maps resulted in a 23% reduction in reading time while maintaining similar recall.