通过将T1加权MRI图像视为反转CT扫描来改善跨模态分割
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
AI总结：
本研究提出将T1加权MRI图像视为反转CT扫描的简单图像反转技术，显著提升CT分割模型在MRI数据上的分割质量，并成功应用于肾癌分割，为跨模态迁移提供快速替代方案。
摘要：
计算机断层扫描（CT）分割模型通常包含磁共振成像（MRI）分割模型当前不支持的类别。在本研究中，我们表明一种简单的图像反转技术可以显著提高CT分割模型在MRI数据上的分割质量。我们针对一个通用的多类别模型和一个特定的肾癌模型，证明了该技术在分割T1加权MRI图像上的可行性。利用该技术，我们能够使用仅基于CT数据训练的模型，在T1加权MRI扫描中定位并分割透明细胞肾细胞癌。图像反转易于实现，且不需要专用的图形处理单元，因此为复杂的深度模态迁移模型提供了一种快速的替代方案。我们的结果表明，现有的CT模型，包括病理模型，可能以合理的努力迁移到MRI领域。
英文摘要：
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.
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