Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge

TL;DR

This paper presents a lightweight deformable registration network that uses adversarial learning with knowledge distillation to achieve high accuracy and efficiency, suitable for CPU deployment in medical imaging tasks.

Contribution

Introduces a novel light-weight deformable registration network leveraging adversarial learning and knowledge distillation for improved efficiency and accuracy.

Findings

Achieves state-of-the-art accuracy on public datasets.

Significantly faster than recent methods.

Effective use of adversarial learning for time-efficient registration.

Abstract

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.