基于旋转等变2D/3D特征匹配的刚性单切片-体积配准
Rigid Single-Slice-in-Volume registration via rotation-equivariant 2D/3D feature matching
AI总结：
提出一种自监督2D/3D刚性配准方法，利用群等变CNN匹配单一切片与3D体积的旋转等变特征，无需解剖先验，并在CT和MRI数据集上取得高精度。
摘要：
2D到3D配准在诊断、手术导航、环境理解、机器人导航、自主系统或增强现实等任务中至关重要。在医学成像中，其目标通常是将2D图像放置到3D体积观测中。当前用于刚性单切片-体积配准的方法受到多种要求的限制，例如位姿初始化、相邻切片堆叠或可靠的解剖标志。本文提出一种自监督2D/3D配准方法，用于将单个2D切片与对应的3D体积进行匹配。该方法适用于没有解剖先验的数据，例如肿瘤图像。它解决了维度差异问题，并通过使用群等变CNN建立2D面内与3D面外旋转等变特征之间的对应关系。这些旋转等变特征从2D查询切片中提取，并与其3D对应特征对齐。结果表明，所提出的切片-体积配准方法在NSCLC-Radiomics CT和KIRBY21 MRI数据集上具有鲁棒性，实现了低于2度的绝对中位角度误差，以及在3像素容差下89%的平均匹配特征准确率。
英文摘要：
2D to 3D registration is essential in tasks such as diagnosis, surgical navigation, environmental understanding, navigation in robotics, autonomous systems, or augmented reality. In medical imaging, the aim is often to place a 2D image in a 3D volumetric observation to w. Current approaches for rigid single slice in volume registration are limited by requirements such as pose initialization, stacks of adjacent slices, or reliable anatomical landmarks. Here, we propose a self-supervised 2D/3D registration approach to match a single 2D slice to the corresponding 3D volume. The method works in data without anatomical priors such as images of tumors. It addresses the dimensionality disparity and establishes correspondences between 2D in-plane and 3D out-of-plane rotation-equivariant features by using group equivariant CNNs. These rotation-equivariant features are extracted from the 2D query slice and aligned with their 3D counterparts. Results demonstrate the robustness of the proposed slice-in-volume registration on the NSCLC-Radiomics CT and KIRBY21 MRI datasets, attaining an absolute median angle error of less than 2 degrees and a mean-matching feature accuracy of 89% at a tolerance of 3 pixels.
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