  In this study, a novel method of data augmentation has been presented for the
segmentation of placental histological images when the labeled data are scarce.
This method generates new realizations of the placenta intervillous morphology
while maintaining the general textures and orientations. As a result, a
diversified artificial dataset of images is generated that can be used for
training deep learning segmentation models. We have observed that on average
the presented method of data augmentation led to a 42% decrease in the binary
cross-entropy loss of the validation dataset compared to the common approach in
the literature. Additionally, the morphology of the intervillous space is
studied under the effect of the proposed image reconstruction technique, and
the diversity of the artificially generated population is quantified. Due to
the high resemblance of the generated images to the real ones, the applications
of the proposed method may not be limited to placental histological images, and
it is recommended that other types of tissues be investigated in future
studies.
