Estimating Phenotypic Characteristics of Tuberculosis Bacteria

Microscopy analysis of sputum images for bacilli screening is a common method used for both diagnosis and therapy monitoring of tuberculosis (TB). Nonetheless, it is a challenging procedure, since sputum examination is time-consuming and needs highly competent personnel to provide accurate results which are important for clinical decision-making. In addition, manual fluorescence microscopy examination of sputum samples for tuberculosis diagnosis and treatment monitoring is a subjective operation. In this work, we automate the process of examining fields of view (FOVs) of TB bacteria in order to determine the lipid content, and bacterial length and width. We propose a modified version of the UNet model to rapidly localise potential bacteria inside a FOV. We introduce a novel method that uses Fourier descriptors to exclude contours that do not belong to the class of bacteria, hence minimising the amount of false positives. Finally, we propose a new feature as a means of extracting a representation fed into a support vector multi-regressor in order to estimate the length and width of each bacterium. Using a real-world data corpus, the proposed method i) outperformed previous methods, and ii) estimated the cell length and width with a root mean square error of less than 0.01%.