multiGradICON: A Foundation Model for Multimodal Medical Image Registration
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913682251841536 |
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| author | Demir, Basar Tian, Lin Greer, Thomas Hastings Kwitt, Roland Vialard, Francois-Xavier Estepar, Raul San Jose Bouix, Sylvain Rushmore, Richard Jarrett Ebrahim, Ebrahim Niethammer, Marc |
| author_facet | Demir, Basar Tian, Lin Greer, Thomas Hastings Kwitt, Roland Vialard, Francois-Xavier Estepar, Raul San Jose Bouix, Sylvain Rushmore, Richard Jarrett Ebrahim, Ebrahim Niethammer, Marc |
| contents | Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration. In this work, we therefore develop multiGradICON as a first step towards universal *multimodal* medical image registration. Specifically, we show that 1) we can train a DL registration model that is suitable for monomodal *and* multimodal registration; 2) loss function randomization can increase multimodal registration accuracy; and 3) training a model with multimodal data helps multimodal generalization. Our code and the multiGradICON model are available at https://github.com/uncbiag/uniGradICON. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00221 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | multiGradICON: A Foundation Model for Multimodal Medical Image Registration Demir, Basar Tian, Lin Greer, Thomas Hastings Kwitt, Roland Vialard, Francois-Xavier Estepar, Raul San Jose Bouix, Sylvain Rushmore, Richard Jarrett Ebrahim, Ebrahim Niethammer, Marc Image and Video Processing Computer Vision and Pattern Recognition Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration. In this work, we therefore develop multiGradICON as a first step towards universal *multimodal* medical image registration. Specifically, we show that 1) we can train a DL registration model that is suitable for monomodal *and* multimodal registration; 2) loss function randomization can increase multimodal registration accuracy; and 3) training a model with multimodal data helps multimodal generalization. Our code and the multiGradICON model are available at https://github.com/uncbiag/uniGradICON. |
| title | multiGradICON: A Foundation Model for Multimodal Medical Image Registration |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.00221 |