MetaMorph：学习具有外观变化的变形图像变换
MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes
摘要：
本文提出了一种新的预测模型MetaMorph，用于具有外观变化的图像的变形配准（即，由脑肿瘤引起）。与以前的基于学习的配准方法相比，这些方法对外观变化几乎没有控制，我们的模型引入了一种新的正则化，可以有效地抑制外观变化区域的负面影响。特别是，我们通过学习异常区域的分割图，在非纯变换的切线空间（也称为初始速度场）上开发了一种分段正则化。几何变换和外观变化被视为共同的任务，是互利的。我们的模型MetaMorph在分割的指导下搜索最佳配准解决方案时更加鲁棒和准确，这反过来又通过提供适当增强的训练标签来提高分割性能。我们在真实的3D人脑肿瘤磁共振成像（MRI）扫描上验证MetaMorph。实验结果表明，我们的模型优于最先进的基于学习的注册模型。所提出的MetaMorph在各种图像引导的临床干预中具有巨大的潜力，例如，用于肿瘤切除手术的实时图像引导导航系统。
英文摘要：
This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration methods that have little or no control over appearance-changes, our model introduces a new regularization that can effectively suppress the negative effects of appearance changing areas. In particular, we develop a piecewise regularization on the tangent space of diffeomorphic transformations (also known as initial velocity fields) via learned segmentation maps of abnormal regions. The geometric transformation and appearance changes are treated as joint tasks that are mutually beneficial. Our model MetaMorph is more robust and accurate when searching for an optimal registration solution under the guidance of segmentation, which in turn improves the segmentation performance by providing appropriately augmented training labels. We validate MetaMorph on real 3D human brain tumor magnetic resonance imaging (MRI) scans. Experimental results show that our model outperforms the state-of-the-art learning-based registration models. The proposed MetaMorph has great potential in various image-guided clinical interventions, e.g., real-time image-guided navigation systems for tumor removal surgery.
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