Purpose: Deformable Image Registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a Deep Convolutional Neural Network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We trained DCNN-Match on pairs of Computed Tomography (CT) scans containing simulated deformations. We explored five variants of DCNN-Match that use different loss functions and assessed their effect on the spatial density of predicted landmarks and the associated matching errors. We also tested DCNN-Match variants in combination with the open-source registration software Elastix to assess the impact of predicted landmarks in providing additional guidance to DIR. Results: We tested our approach on lower-abdominal CT scans from cervical cancer patients: 121 pairs containing simulated deformations and 11 pairs demonstrating clinical deformations. The results showed significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in the case of simulated (p = $0e^0$) as well as clinical deformations (p = 0.030). We also observed that the spatial density of the automatic landmarks with respect to the underlying deformation affect the extent of improvement in DIR. Finally, DCNN-Match was found to generalize to Magnetic Resonance Imaging (MRI) scans without requiring retraining, indicating easy applicability to other datasets. Conclusions: DCNN-Match learns to predict landmark correspondences in 3D medical images in a self-supervised manner, which can improve DIR performance.

翻译：目的：可变形图像配准（DIR）可通过利用图像中对应地标的额外引导而受益，然而这种方法的优势尚未得到充分研究，尤其是缺乏针对三维（3D）医学图像的自动地标检测方法。方法：我们提出一种名为DCNN-Match的深度卷积神经网络（DCNN），该网络以自监督方式学习预测3D图像中的地标对应关系。我们使用包含模拟变形的计算机断层扫描（CT）图像对训练DCNN-Match，探索了五种采用不同损失函数的DCNN-Match变体，并评估其对预测地标空间密度及相关匹配误差的影响。同时，我们将DCNN-Match变体与开源配准软件Elastix结合使用，以评估预测地标为DIR提供额外引导的效果。结果：我们使用宫颈癌患者的下腹部CT扫描数据测试方法：121对包含模拟变形的图像对以及11对展现临床变形的图像对。结果显示，无论针对模拟变形（p = $0e^0 $）还是临床变形（p = 0.030），使用DCNN-Match预测的地标对应关系均显著提升了DIR性能。我们还观察到，自动地标相对于底层变形的空间密度直接影响DIR的改善程度。此外，DCNN-Match在无需重新训练的情况下即可泛化至磁共振成像（MRI）扫描，表明其易于应用于其他数据集。结论：DCNN-Match以自监督方式学习预测3D医学图像中的地标对应关系，从而提升DIR性能。