Transformer in Microbial Image Analysis: A Comparative Exploration of TransUNet, UNet, and DoubleUNet for SEM Image Segmentation

The advent of transformer-based architectures such as TransUNet has revolutionized image segmentation as this approach combines the strengths of transformers for capturing contextual information with convolutional neural networks (CNNs) for localized feature identification. Microbes, known for their complex behaviors, present challenges in various fields, especially biomedicine. Image segmentation is crucial for ana-lyzing microbes, allowing quantitative analysis, growth tracking, and understanding host-pathogen interactions. This study is dedicated to a comparative analysis of TransUNet alongside two other popular segmentation methods, UNet and DoubleUNet, in the context of segmenting scanning electron microscope (SEM) images of microbes on layered graphene-nickel specimens. The TransUNet architecture employs a pre-defined ResNet-50 and Vision Transformer (ViT) as the encoder and a custom-built decoder trained on SEM data of Oleidesulfovibrio alaskensis (OA-G20) exposed to graphene-nickel specimens for 30 days. Using the Intersection Over Union (IoU) score as a performance metric, we observed that TransUNet achieved a maximum IoU of 79.58%, DoubleUNet exhibited a maximum IoU of 76.28%, and UNet attained a maximum IoU of 72.38%. We believe that this comparative study of the segmentation approach is invaluable for selecting the best model for the practitioner as per need. This study is the first step in our aim of developing an end-to-end framework with automated model selection based on dataset characteristics for microbial image segmentation.