Multi-modal deformable image registration using untrained neural networks

• URL: http://arxiv.org/abs/2411.02672v2
• Date: Mon, 27 Jan 2025 06:08:39 GMT
• Title: Multi-modal deformable image registration using untrained neural networks
• Authors: Quang Luong Nhat Nguyen, Ruiming Cao, Laura Waller,
• Abstract summary: We propose a registration method that utilizes neural networks for image representation. Our method uses untrained networks with limited representation capacity as an implicit prior to a good registration. Unlike previous approaches that are specialized for specific data types, our method handles both rigid and non-rigid, as well as single- and multi-modal registration.
• Score: 3.0832643041058603
• License:
• Abstract: Image registration techniques usually assume that the images to be registered are of a certain type (e.g. single- vs. multi-modal, 2D vs. 3D, rigid vs. deformable) and there lacks a general method that can work for data under all conditions. We propose a registration method that utilizes neural networks for image representation. Our method uses untrained networks with limited representation capacity as an implicit prior to guide for a good registration. Unlike previous approaches that are specialized for specific data types, our method handles both rigid and non-rigid, as well as single- and multi-modal registration, without requiring changes to the model or objective function. We have performed a comprehensive evaluation study using a variety of datasets and demonstrated promising performance.

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