Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
<|reference_start|>Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans: 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.<|reference_end|>
@article{häntze2024improve,
    title={Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images
  as Inverted CT Scans},
    author={Hartmut H"antze, Lina Xu, Maximilian Rattunde, Leonhard Donle, Felix
  J. Dorfner, Alessa Hering, Lisa C. Adams, Keno K. Bressem},
    journal={arXiv preprint arXiv:2405.03713},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03713},
    primaryClass={eess.IV cs.CV cs.LG}
}