Impact of PCA-based preprocessing and different CNN structures on
deformable registration of sonograms
- URL: http://arxiv.org/abs/2301.08802v1
- Date: Fri, 20 Jan 2023 21:01:39 GMT
- Title: Impact of PCA-based preprocessing and different CNN structures on
deformable registration of sonograms
- Authors: Christian Schmidt and Heinrich Martin Overhoff
- Abstract summary: Central venous catheters (CVCs) are commonly inserted into the large veins of the neck.
In this work, a linear, affine transformation is performed on cervical sonograms, followed by a non-linear transformation to achieve a more precise registration.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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