We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard. The framework we design includes a parameter specification module to efficiently choose initialization pose parameter and a fine-registration module to align images. The proposed framework takes extracting multi-scale features into consideration using a novel composite connection encoder with special training techniques. We compare the method with both learning-based methods and optimization-based methods on a in-house CT/X-ray dataset as well as simulated data to further evaluate performance. Our experiments demonstrate that the method in this paper has improved the registration performance, and thereby outperforms the existing methods in terms of accuracy and running time. We also show the potential of the proposed method as an initial pose estimator. The code is available at https://github.com/m1nhengChen/SOPI

翻译：我们提出了一种新颖的基于深度学习的框架：特定参数初始化的嵌入式特征相似度优化方法（SOPI），用于解决2D/3D医学图像配准这一极具挑战性的问题，其难点包括维度不匹配、计算负荷大以及缺乏金标准评估准则。我们设计的框架包含一个参数规范模块，用于高效选择初始化位姿参数，以及一个精细配准模块用于图像对齐。该框架通过采用一种具有特殊训练技术的新型复合连接编码器，充分考虑了多尺度特征提取。我们将该方法与基于学习的方法和基于优化的方法，在内部CT/X射线数据集及模拟数据上进行了对比，以进一步评估其性能。实验表明，本文方法在配准性能上得到提升，从而在准确性和运行时间方面优于现有方法。我们还展示了所提方法作为初始位姿估计器的潜力。代码已开源：https://github.com/m1nhengChen/SOPI