Resolution enhancement of placenta histological images using deep learning

TL;DR

This paper presents a deep learning approach using a modified U-net to enhance the resolution of placenta histological images, improving detail and contrast to better analyze tissue structures.

Contribution

A novel deep neural network model tailored for placenta image resolution enhancement, trained on paired high- and low-resolution images, achieving high accuracy and improved image quality.

Findings

Achieved a relative mean squared error of 0.003 on test images.

Enhanced contrast and added details in low-resolution images.

Demonstrated effective resolution improvement for placenta histology.

Abstract

In this study, a method has been developed to improve the resolution of histological human placenta images. For this purpose, a paired series of high- and low-resolution images have been collected to train a deep neural network model that can predict image residuals required to improve the resolution of the input images. A modified version of the U-net neural network model has been tailored to find the relationship between the low resolution and residual images. After training for 900 epochs on an augmented dataset of 1000 images, the relative mean squared error of 0.003 is achieved for the prediction of 320 test images. The proposed method has not only improved the contrast of the low-resolution images at the edges of cells but added critical details and textures that mimic high-resolution images of placenta villous space.