Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916965744902144 |
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| author | Häntze, Hartmut Xu, Lina Rattunde, Maximilian Donle, Leonhard Dorfner, Felix J. Hering, Alessa Adams, Lisa C. Bressem, Keno K. |
| author_facet | Häntze, Hartmut Xu, Lina Rattunde, Maximilian Donle, Leonhard Dorfner, Felix J. Hering, Alessa Adams, Lisa C. Bressem, Keno K. |
| contents | Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show that a simple image inversion technique can significantly improve the segmentation quality of CT segmentation models on MRI data. We demonstrate the feasibility for both a general multi-class and a specific renal carcinoma model for segmenting T1-weighted MRI images. Using this technique, we were able to localize and segment clear cell renal cell carcinoma in T1-weighted MRI scans, using a model that was trained on only CT data. Image inversion is straightforward to implement and does not require dedicated graphics processing units, thus providing a quick alternative to complex deep modality-transfer models. Our results demonstrate that existing CT models, including pathology models, might be transferable to the MRI domain with reasonable effort. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03713 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans Häntze, Hartmut Xu, Lina Rattunde, Maximilian Donle, Leonhard Dorfner, Felix J. Hering, Alessa Adams, Lisa C. Bressem, Keno K. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning J.3 Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show that a simple image inversion technique can significantly improve the segmentation quality of CT segmentation models on MRI data. We demonstrate the feasibility for both a general multi-class and a specific renal carcinoma model for segmenting T1-weighted MRI images. Using this technique, we were able to localize and segment clear cell renal cell carcinoma in T1-weighted MRI scans, using a model that was trained on only CT data. Image inversion is straightforward to implement and does not require dedicated graphics processing units, thus providing a quick alternative to complex deep modality-transfer models. Our results demonstrate that existing CT models, including pathology models, might be transferable to the MRI domain with reasonable effort. |
| title | Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning J.3 |
| url | https://arxiv.org/abs/2405.03713 |