  In medical image diagnosis, pathology image analysis using semantic
segmentation becomes important for efficient screening as a field of digital
pathology. The spatial augmentation is ordinary used for semantic segmentation.
Tumor images under malignant are rare and to annotate the labels of nuclei
region takes much time-consuming. We require an effective use of dataset to
maximize the segmentation accuracy. It is expected that some augmentation to
transform generalized images influence the segmentation performance. We propose
a synthetic augmentation using label-to-image translation, mapping from a
semantic label with the edge structure to a real image. Exactly this paper deal
with stain slides of nuclei in tumor. Actually, we demonstrate several
segmentation algorithms applied to the initial dataset that contains real
images and labels using synthetic augmentation in order to add their
generalized images. We computes and reports that a proposed synthetic
augmentation procedure improve their accuracy.
