Automated segmentation and morphological characterization of placental histology images based on a single labeled image

TL;DR

This paper introduces a novel data augmentation method for placental histology image segmentation, significantly improving model performance by generating diverse, realistic artificial images from limited labeled data.

Contribution

A new data augmentation technique that enhances placental image segmentation by creating realistic, diverse images, addressing scarcity of labeled data.

Findings

42% reduction in binary cross-entropy loss

High resemblance of generated images to real data

Potential applicability to other tissue types

Abstract

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.