用于提升基于关键点配准的准确性和可解释性的空间正则化
Spatial regularisation for improved accuracy and interpretability in keypoint-based registration
AI总结：
针对无监督关键点配准中特征空间分布扩散导致可解释性差的问题，提出一种三重损失（KL散度建模、空间锐化和排斥损失）进行空间正则化，在提升特征可解释性的同时显著提高了配准精度。
摘要：
无监督配准策略通过优化固定体积和移动体积之间的相似性度量，绕过了对真实变换或分割的需求。在这些方法中，最近基于无监督关键点检测的一个子类方法在可解释性方面表现出极大的前景。具体而言，这些方法训练网络预测固定和移动图像的特征图，从中计算可解释的质心以获取点云，然后以闭式形式进行对齐。然而，网络返回的特征通常产生难以解释的空间扩散模式，从而削弱了基于关键点配准的目的。在此，我们提出一种三重损失来正则化特征的空间分布。首先，我们使用KL散度将特征建模为点扩散函数，并将其解释为概率关键点。然后，我们锐化这些特征的空间分布，以提高检测到的标志点的精度。最后，我们引入一种新的跨关键点排斥损失，以鼓励空间多样性。总体而言，我们的损失显著提高了特征的可解释性，使其现在对应于精确且具有解剖学意义的标志点。我们在胎儿刚体运动跟踪和脑MRI仿射配准任务中展示了我们的三重损失，它不仅优于最先进的无监督策略，还缩小了与最先进的有监督方法之间的差距。我们的代码可在 https://github.com/BenBillot/spatial_regularisation 获取。
英文摘要：
Unsupervised registration strategies bypass requirements in ground truth transforms or segmentations by optimising similarity metrics between fixed and moved volumes. Among these methods, a recent subclass of approaches based on unsupervised keypoint detection stand out as very promising for interpretability. Specifically, these methods train a network to predict feature maps for fixed and moving images, from which explainable centres of mass are computed to obtain point clouds, that are then aligned in closed-form. However, the features returned by the network often yield spatially diffuse patterns that are hard to interpret, thus undermining the purpose of keypoint-based registration. Here, we propose a three-fold loss to regularise the spatial distribution of the features. First, we use the KL divergence to model features as point spread functions that we interpret as probabilistic keypoints. Then, we sharpen the spatial distributions of these features to increase the precision of the detected landmarks. Finally, we introduce a new repulsive loss across keypoints to encourage spatial diversity. Overall, our loss considerably improves the interpretability of the features, which now correspond to precise and anatomically meaningful landmarks. We demonstrate our three-fold loss in foetal rigid motion tracking and brain MRI affine registration tasks, where it not only outperforms state-of-the-art unsupervised strategies, but also bridges the gap with state-of-the-art supervised methods. Our code is available at https://github.com/BenBillot/spatial_regularisation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
