Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms

TL;DR

This study evaluates how PCA-based denoising and CNN architecture modifications affect deformable registration accuracy of cervical sonograms, demonstrating that PCA improves registration and smaller networks can be sufficient.

Contribution

It introduces the impact of PCA preprocessing and CNN structure complexity on deformable registration performance in sonogram images.

Findings

PCA denoising reduces deformation lengths by up to 66%.

Simpler CNN structures can achieve comparable registration quality.

Reduced network complexity does not compromise registration accuracy.

Abstract

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.