Multi-modal deformable image registration using untrained neural networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910800742973440 |
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| author | Nguyen, Quang Luong Nhat Cao, Ruiming Waller, Laura |
| author_facet | Nguyen, Quang Luong Nhat Cao, Ruiming Waller, Laura |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-modal deformable image registration using untrained neural networks Nguyen, Quang Luong Nhat Cao, Ruiming Waller, Laura Image and Video Processing Machine Learning 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. |
| title | Multi-modal deformable image registration using untrained neural networks |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2411.02672 |