基于PCA的预处理和不同CNN结构对声图变形配准的影响
Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms
摘要：
中心静脉导管（CVC）通常插入颈部的大静脉，例如颈内静脉（IJV）。CVC插入可能会导致严重的并发症，如错位到动脉或颈部血管穿孔。在超声引导下放置CVC是减少此类不良事件的适当方法，如果能够可靠地检测到静脉和动脉血管等解剖标志。应通过配准患者个体图像与解剖标记的参考图像来解决该任务。在这项工作中，一个线性，仿射变换进行宫颈超声，然后通过非线性变换，以实现更精确的注册。体素形态（VM），一个基于学习的库的变形图像配准使用卷积神经网络（CNN）与U-Net结构被用于非线性变换。基于主成分分析（PCA）的患者个体图像预去噪的影响，以及具有不同复杂性的修改后的网络结构对配准结果的影响进行了视觉和定量检查，后者使用变形和图像相似性的度量。使用PCA近似的宫颈超声检查导致平均变形长度减少18%至66%之间，与原始图像相比，这取决于网络结构。此外，减少卷积层的数量导致与PCA图像的图像相似性提高，而原始图像则恶化。尽管大量减少了网络参数，但没有观察到配准质量的整体下降，从而得出结论，即原始网络结构对于手头的任务来说过大。
英文摘要：
Central venous catheters (CVC) are commonly inserted into the large veins of the neck, e.g. the internal jugular vein (IJV). CVC insertion may cause serious complications like misplacement into an artery or perforation of cervical vessels. Placing a CVC under sonographic guidance is an appropriate method to reduce such adverse events, if anatomical landmarks like venous and arterial vessels can be detected reliably. This task shall be solved by registration of patient individual images vs. an anatomically labelled reference image. In this work, a linear, affine transformation is performed on cervical sonograms, followed by a non-linear transformation to achieve a more precise registration. Voxelmorph (VM), a learning-based library for deformable image registration using a convolutional neural network (CNN) with U-Net structure was used for non-linear transformation. The impact of principal component analysis (PCA)-based pre-denoising of patient individual images, as well as the impact of modified net structures with differing complexities on registration results were examined visually and quantitatively, the latter using metrics for deformation and image similarity. Using the PCA-approximated cervical sonograms resulted in decreased mean deformation lengths between 18% and 66% compared to their original image counterparts, depending on net structure. In addition, reducing the number of convolutional layers led to improved image similarity with PCA images, while worsening in original images. Despite a large reduction of network parameters, no overall decrease in registration quality was observed, leading to the conclusion that the original net structure is oversized for the task at hand.
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