A Deep Learning Approach to Enhance Semantic Segmentation of Bacteria and Pus Cells from Microscopic Urine Smear Images Using Synthetic Data

Urine smear analysis aids in preliminary diagnosis of Urinary Tract Infection. But it is time-consuming and requires a lot of medical expertise. Automating the process using machine learning can save time and effort. However obtaining a large medical dataset is difficult due to data privacy concerns and medical expertise requirements. In this study, we propose a method to synthesize a large dataset of gram-stained microscopic images containing pus cells and bacteria. We train a machine learning model to achieve semantic segmentation of bacteria and pus cells using this dataset. Later we use it to perform transfer learning on a relatively small dataset of gram stained urine microscopic images. Our approach improved the F1-score from 50% to 63% for bacteria segmentation and from 77% to 83% for pus cell segmentation. This method has the potential to improve the turn-around time and the quality of preliminary diagnosis of Urinary Tract Infection.