Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty
Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty
复制标题
通过深度多类分类进行概率图像配准：表征不确定性
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
W. Wells
中科院分区：
文献类型：
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作者：
A. Sedghi;T. Kapur;Jie Luo;P. Mousavi;W. Wells
我们提出了一种新的概率图像配准方法，该方法利用深度学习的优势来建模图像之间的一致性。我们使用一个深度多类分类器，在不同类别的补丁对上训练，包括不相关的，注册的，以及补丁之间的离散位移的集合。位移类通过梯度下降减轻了对配准时间优化的需要;相反，后验概率用于直接预测采样位置的晶格上的位移的预期值。这些又用于更新转换参数，并迭代该过程。我们凭经验证明了我们提出的方法的准确性可变形的跨模态配准的脑MRI，并显示改进的结果相比，基于互信息的方法具有挑战性的数据，包括模拟切除。我们的方法能够对配准不确定性和诊断进行局部预测，这些预测和诊断可以指示两个图像中似乎不相关的区域。不确定性估计为最终用户提供了关于介入和手术环境中配准质量的直观可操作信息。
We present a novel approach to probabilistic image registration that leverages the strengths of deep-learning for modeling agreement between images. We use a deep multi-class classifier trained on different classes of patch pairs, including unrelated, registered, and a collection of discrete displacements between patches. The displacement classes alleviate the need for registration-time optimization by gradient descent; instead, posterior probabilities are used to directly predict expected values of displacements on the lattice of sampled locations. These, in turn, are used to update transformation parameters and the process is iterated. We empirically demonstrate the accuracy of our proposed method on deformable cross-modality registrations of brain MRI, and show improved results compared to Mutual Information based method on challenging data that includes simulated resections. Our approach enables local predictions of registration uncertainty and diagnostics that can indicate areas that seem unrelated in the two images. Uncertainty estimates provide end-users with intuitively actionable information on the quality of registration in interventional and surgical settings.