Improve Cross-Modality Segmentation by Treating MRI Images as Inverted CT Scans

• URL: http://arxiv.org/abs/2405.03713v1
• Date: Sat, 4 May 2024 14:02:52 GMT
• Title: Improve Cross-Modality Segmentation by Treating MRI Images as Inverted CT Scans
• Authors: Hartmut Häntze, Lina Xu, Leonhard Donle, Felix J. Dorfner, Alessa Hering, Lisa C. Adams, Keno K. Bressem,
• Abstract summary: We show that a simple image inversion technique can significantly improve the segmentation quality of CT segmentation models on MRI data. Image inversion is straightforward to implement and does not require dedicated graphics processing units (GPUs)
• Score: 0.4867169878981935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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