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Deformable registration is a crucial step in many medical procedures such asimage-guided surgery and radiation therapy. Most recent learning-based methodsfocus on improving the accuracy by optimizing the non-linear spatialcorrespondence between the input images. Therefore, these methods arecomputationally expensive and require modern graphic cards for real-timedeployment. In this paper, we introduce a new Light-weight DeformableRegistration network that significantly reduces the computational cost whileachieving competitive accuracy. In particular, we propose a new adversariallearning with distilling knowledge algorithm that successfully leveragesmeaningful information from the effective but expensive teacher network to thestudent network. We design the student network such as it is light-weight andwell suitable for deployment on a typical CPU. The extensively experimentalresults on different public datasets show that our proposed method achievesstate-of-the-art accuracy while significantly faster than recent methods. Wefurther show that the use of our adversarial learning algorithm is essentialfor a time-efficiency deformable registration method. Finally, our source codeand trained models are available at: https://github.com/aioz-ai/LDR_ALDK.

Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge