تقرير
Improve Cross-Modality Segmentation by Treating MRI Images as Inverted CT Scans
| العنوان: | Improve Cross-Modality Segmentation by Treating MRI Images as Inverted CT Scans |
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| المؤلفون: | Häntze, Hartmut, Xu, Lina, Donle, Leonhard, Dorfner, Felix J., Hering, Alessa, Adams, Lisa C., Bressem, Keno K. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, J.3 |
| الوصف: | Computed tomography (CT) segmentation models frequently include 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, by using the TotalSegmentator model, applied to T1-weighted MRI images, as example. Image inversion is straightforward to implement and does not require dedicated graphics processing units (GPUs), thus providing a quick alternative to complex deep modality-transfer models for generating segmentation masks for MRI data. Comment: 3 pages, 2 figures |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2405.03713Test |
| رقم الانضمام: | edsarx.2405.03713 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |