Using Deep Learning Super-Resolution for Improved Segmentation of SEM Biofilm Images

Abstract

Scanning electron microscopy (SEM) images play a crucial role in the quantitative analyses of biofilms on materials by providing detailed information about biofilm formation, ultrastructure, cells and their interaction with materials. In addition to some intrinsic limitations of SEM imaging, some of the characteristics of images in SEM volumes often vary in terms of the wide range of magnifications, high resolutions, depth of the field, and the SEM protocols. Quantitative characterization of biofilm morphologies such as the cell size, geometry, and density from these SEM volumes is a challenging problem. This paper presents deep learning based super-resolution (DLSR) as a step towards addressing this problem. Three DLSR approaches based on generative adversarial learning techniques are applied to an SEM biofilm dataset and compared in terms of their a) preservation of the morphological features, and b) their impact on the performance of a deep learning based image segmentation task. Our results show that the DLSR approaches vary in preservation of morphological features. We also show that DLSR can considerably improve the performance of image segmentation outputs of deep learning networks and hence be incorporated in any deep learning pipeline used for quantitative analyses of biofilms based on SEM images.