IEEE J Biomed Health Inform. 2021 Sep;25(9):3300-3309. doi: 10.1109/JBHI.2020.3045977. Epub 2021 Sep 3.
Cardiovascular image registration is an essential approach to combine the advantages of preoperative 3D computed tomography angiograph (CTA) images and intraoperative 2D X-ray/digital subtraction angiography (DSA) images together in minimally invasive vascular interventional surgery (MIVI). Recent studies have shown that convolutional neural network (CNN) regression model can be used to register these two modality vascular images with fast speed and satisfactory accuracy. However, CNN regression model trained by tens of thousands of images of one patient is often unable to be applied to another patient due to the large difference and deformation of vascular structure in different patients. To overcome this challenge, we evaluate the ability of transfer learning (TL) for the registration of 2D/3D deformable cardiovascular images. Frozen weights in the convolutional layers were optimized to find the best common feature extractors for TL. After TL, the training data set size was reduced to 200 for a randomly selected patient to get accurate registration results. We compared the effectiveness of our proposed nonrigid registration model after TL with not only that without TL but also some traditional intensity-based methods to evaluate that our nonrigid model after TL performs better on deformable cardiovascular image registration.
心血管图像配准是将微创血管介入手术（MIVI）中术前三维计算机断层血管造影（CTA）图像和术中二维 X 射线/数字减影血管造影（DSA）图像的优势结合起来的一种重要方法。最近的研究表明，卷积神经网络（CNN）回归模型可用于快速准确地配准这两种模式的血管图像。然而，由于不同患者的血管结构存在较大差异和变形，经过数千张同一患者图像训练的 CNN 回归模型通常无法应用于其他患者。为了克服这一挑战，我们评估了迁移学习（TL）在 2D/3D 可变形心血管图像配准中的能力。我们优化了卷积层中的冻结权重，以找到用于 TL 的最佳公共特征提取器。经过 TL 后，将训练数据集的大小减少到随机选择的患者的 200 个，以获得准确的配准结果。我们比较了 TL 前后提出的非刚性配准模型的有效性，不仅与没有 TL 的模型进行了比较，还与一些传统的基于强度的方法进行了比较，以评估经过 TL 的非刚性模型在可变形心血管图像配准方面的性能更好。
