Light-Weight Deformable Registration Using Adversarial Learning With Distilling Knowledge.

Tran, Minh Q, Do, Tuong, Tran, Huy, Tjiputra, Erman, Tran, Quang D and Nguyen, Anh ORCID: 0000-0002-1449-211X (2022) Light-Weight Deformable Registration Using Adversarial Learning With Distilling Knowledge. IEEE transactions on medical imaging, 41 (6). pp. 1443-1453.

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Official URL: http://dx.doi.org/10.1109/tmi.2022.3141013

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.

Item Type:	Article
Uncontrolled Keywords:	Humans, Algorithms, Image Processing, Computer-Assisted, Software
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	14 Jan 2022 08:57
Last Modified:	18 Jan 2023 21:16
DOI:	10.1109/tmi.2022.3141013
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3146302