ArXiv Preprint
To facilitate both the detection and the interpretation of findings in chest
X-rays, comparison with a previous image of the same patient is very valuable
to radiologists. Today, the most common approach for deep learning methods to
automatically inspect chest X-rays disregards the patient history and
classifies only single images as normal or abnormal. Nevertheless, several
methods for assisting in the task of comparison through image registration have
been proposed in the past. However, as we illustrate, they tend to miss
specific types of pathological changes like cardiomegaly and effusion. Due to
assumptions on fixed anatomical structures or their measurements of
registration quality they tend to produce unnaturally deformed warp fields
impacting visualization of the difference image between moving and fixed
images. To overcome these limitations, we are the first to use a new paradigm
based on individual rib pair segmentation for anatomy penalized registration,
which proves a natural way to limit folding of the warp field, especially
beneficial for image pairs with large pathological changes. We show that it is
possible to develop a deep learning powered solution that can visualize what
other methods overlook on a large data set of paired public images, starting
from less than 25 fully labeled and 50 partly labeled training images,
employing sequential instance memory segmentation with hole dropout, weak
labeling, coarse-to-fine refinement and Gaussian mixture model histogram
matching. We statistically evaluate the benefits of our method over the SOTA
and highlight the limits of currently used metrics for registration of chest
X-rays.
Astrid Berg, Eva Vandersmissen, Maria Wimmer, David Major, Theresa Neubauer, Dimitrios Lenis, Jeroen Cant, Annemiek Snoeckx, Katja Bühler
2023-01-23
