Adversarial Learning Based Semi-supervised Semantic Segmentation of Low Resolution Gram Stained Microscopic Images

Urinary tract infections (UTIs) are infections that affect the urinary system. It is usually caused by bacteria and pus cells. Analyzing urine samples, including examining pus cells, is a standard method for diagnosing and monitoring UTIs. However, manually detecting bacteria or pus cells in microscopic urine images is a time-consuming and labour-intensive task for microbiologists. Therefore, the segmentation of microscopic pus cell images will ease the process of detecting UTI. Especially low resolution microscopic images are hard to annotate; therefore, in this study, we propose an adversarial learning based semi-supervised segmentation method for segmentation of pus cell images at low resolution i.e. 40 \(\times \) using labeled high resolution images i.e. 100 \(\times \) . The proposed methodology aims to ease the process of UTI detection by automating the segmentation of pus cell images. The results of the proposed methodology demonstrate an increase in the Dice coefficient score percentage by 1 \(\%\) , 1.6 \(\%\) and 2.4 \(\%\) on 40 \(\times \) images when compared to fully supervised segmentation model trained on only 100 \(\times \) data using three different architectures- Unet, ResUnet++, and PSPnet, respectively.