Individual factors that result in susceptibility and morbidity due to Mycobacterium tuberculosis (M.tb) infection are not clearly defined in humans or in animal models of disease. This leads to challenges for example, differentiating life-long control of latent Mycobacterium tuberculosis infection from active tuberculosis disease and accurately predicting who will develop active tuberculosis. As a part of a larger study to identify biomarkers to differentiate these states and predict long-term clinical outcomes, here we present a deep learning approach to segment Mycobacterium tuberculosis bacilli clusters from acid-fast stained lung sections from experimentally infected mice. In a 4-fold cross-validation of 178 slides, our method demonstrates ability to segment bacilli with median dice coefficient of 0.8. In tandem with improved segmentation and cluster analysis association with specific anatomical regions, our method will be able to differentiate between clinical states of M.tb infection.