Meta-Learning Initializations for Interactive Medical Image Registration

• URL: http://arxiv.org/abs/2210.15371v1
• Date: Thu, 27 Oct 2022 12:30:53 GMT
• Title: Meta-Learning Initializations for Interactive Medical Image Registration
• Authors: Zachary M.C. Baum, Yipeng Hu, Dean Barratt
• Abstract summary: This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application. Applying sparsely sampled data to non-interactive methods yields higher registration errors (6.26 mm), demonstrating the effectiveness of interactive MR-TRUS registration.
• Score: 0.18750851274087482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ultrasound (TRUS) images. Our approach obtains comparable registration error (4.26 mm) to the best-performing non-interactive learning-based 3D-to-3D method (3.97 mm) while requiring only a fraction of the data, and occurring in real-time during acquisition. Applying sparsely sampled data to non-interactive methods yields higher registration errors (6.26 mm), demonstrating the effectiveness of interactive MR-TRUS registration, which may be applied intraoperatively given the real-time nature of the adaptation process.

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