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Recent research in perinatal pathology argues that analyzing properties of the placenta may reveal important
information on how certain diseases progress. One important property is the structure of the placental fetal
stems. Analysis of the fetal stems in a placenta could be useful in the study and diagnosis of some diseases
like autism. To study the fetal stem structure effectively, we need to automatically and accurately track fetal
stems through a sequence of digitized hematoxylin and eosin (H&E) stained histology slides. There are many
problems in successfully achieving this goal. A few of the problems are: large size of images, misalignment of
the consecutive H&E slides, unpredictable inaccuracies of manual tracing, very complicated texture patterns of
various tissue types without clear characteristics, just to name a few. In this paper we propose a novel algorithm
to achieve automatic tracing of the fetal stem in a sequence of H&E images, based on an inaccurate manual
segmentation of a fetal stem in one of the images. This algorithm combines global affine registration, local
non-affine registration and a novel 'dynamic' version of the active contours model without edges. We first use
global affine image registration of all the images based on displacement, scaling and rotation. This gives us
approximate location of the corresponding fetal stem in the image that needs to be traced. We then use the
affine registration algorithm "locally" near this location. At this point, we use a fast non-affine registration
based on L2-similarity measure and diffusion regularization to get a better location of the fetal stem. Finally, we
have to take into account inaccuracies in the initial tracing. This is achieved through a novel dynamic version of
the active contours model without edges where the coefficients of the fitting terms are computed iteratively to
ensure that we obtain a unique stem in the segmentation. The segmentation thus obtained can then be used as
an initial guess to obtain segmentation in the rest of the images in the sequence. This constitutes an important
step in the extraction and understanding of the fetal stem vasculature.
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