Generative Synthetic Augmentation using Label-to-Image Translation for Nuclei Image Segmentation
This work addresses the challenge of rare malignant tumor images and time-consuming annotation in digital pathology, offering an incremental improvement for nuclei segmentation.
The paper tackles the problem of limited annotated tumor nuclei images for semantic segmentation by proposing a synthetic augmentation method using label-to-image translation, which improves segmentation accuracy as demonstrated on stain slides of nuclei.
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.