Unsupervised 3D registration through optimization-guided cyclical self-training

• URL: http://arxiv.org/abs/2306.16997v2
• Date: Thu, 20 Jul 2023 07:29:03 GMT
• Title: Unsupervised 3D registration through optimization-guided cyclical self-training
• Authors: Alexander Bigalke, Lasse Hansen, Tony C. W. Mok, Mattias P. Heinrich
• Abstract summary: State-of-the-art deep learning-based registration methods employ three different learning strategies. We propose a novel self-supervised learning paradigm for unsupervised registration, relying on self-training. We evaluate the method for abdomen and lung registration, consistently surpassing metric-based supervision and outperforming diverse state-of-the-art competitors.
• Score: 71.75057371518093
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised learning, which heavily relies on hand-crafted similarity metrics designed by domain experts, or learning from synthetic data, which introduces a domain shift. To overcome the limitations of these strategies, we propose a novel self-supervised learning paradigm for unsupervised registration, relying on self-training. Our idea is based on two key insights. Feature-based differentiable optimizers 1) perform reasonable registration even from random features and 2) stabilize the training of the preceding feature extraction network on noisy labels. Consequently, we propose cyclical self-training, where pseudo labels are initialized as the displacement fields inferred from random features and cyclically updated based on more and more expressive features from the learning feature extractor, yielding a self-reinforcement effect. We evaluate the method for abdomen and lung registration, consistently surpassing metric-based supervision and outperforming diverse state-of-the-art competitors. Source code is available at https://github.com/multimodallearning/reg-cyclical-self-train.

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