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 registration 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 comparable 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.