  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 produce unnaturally deformed warp fields impacting
visualization of differences between moving and fixed images. We aim to
overcome these limitations, through a new paradigm based on individual rib pair
segmentation for anatomy penalized registration. Our method proves to be a
natural way to limit the folding percentage of the warp field to 1/6 of the
state of the art while increasing the overlap of ribs by more than 25%,
implying difference images showing pathological changes overlooked by other
methods. We develop an anatomically penalized convolutional multi-stage
solution on the National Institutes of Health (NIH) data set, starting from
less than 25 fully 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 and highlight the limits of currently used
metrics for registration of chest X-rays.
