Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge
This work addresses the need for real-time, efficient deformable registration in medical procedures like image-guided surgery, offering a deployable solution on typical CPUs, though it is incremental as it builds on existing learning-based methods.
The paper tackles the problem of computationally expensive deformable registration in medical imaging by introducing a light-weight network that reduces computational cost while achieving competitive accuracy, with results showing state-of-the-art accuracy and significant speed improvements over recent methods.
Deformable registration is a crucial step in many medical procedures such as image-guided surgery and radiation therapy. Most recent learning-based methods focus on improving the accuracy by optimizing the non-linear spatial correspondence between the input images. Therefore, these methods are computationally expensive and require modern graphic cards for real-time deployment. In this paper, we introduce a new Light-weight Deformable Registration network that significantly reduces the computational cost while achieving competitive accuracy. In particular, we propose a new adversarial learning with distilling knowledge algorithm that successfully leverages meaningful information from the effective but expensive teacher network to the student network. We design the student network such as it is light-weight and well suitable for deployment on a typical CPU. The extensively experimental results on different public datasets show that our proposed method achieves state-of-the-art accuracy while significantly faster than recent methods. We further show that the use of our adversarial learning algorithm is essential for a time-efficiency deformable registration method. Finally, our source code and trained models are available at: https://github.com/aioz-ai/LDR_ALDK.