Multi-modal deformable image registration using untrained neural networks
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 netw...
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| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
04-11-2024
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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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| DOI: | 10.48550/arxiv.2411.02672 |