Automated monitoring of Mycobacterium population growth in time-lapse microscopy with Deep Learning pseudo-supervised algorithm

The understanding of bacteria evolution through time and the analysis of their interaction with the medium is a main step in biomedical research. Bacteria cause in humans different infectious diseases, such as tuberculosis. Tuberculosis is one of the leading causes of death worldwide, caused by Mycobacterium tuberculosis ( Mtb ). Observation of Mycobacteria under a microscope provides essential information for disease-fighting. Nevertheless, Mtb presents a very particular cell division pattern characterized by a high entanglement in which the frontier between dividing cells are almost indistinguishable to the human eye, which makes the manual analysis of cultures a very challenging task, resulting in extremely long processing times. In this paper, we propose a novel approach to automatically detect bacterial cells and infer the growth rate and growth speed over a time-lapse microscopy (TLM) sequence. This method leverages a semi-supervised Deep Learning approach with a lightweight U-Net for efficient and fast processing. To reduce efforts of database construction, we deploy and exploit a mix of manually and synthetically annotated databases of two different classes of Mycobacterium , i.e. Mtb and Mycobacterium smegmatis ( Msm ). The experiments demonstrated the efficiency of the detection of both bacterial reproduction rate and bacterial death in Mycobacterium species, with the Phase-contrast (PhC) microscopy channels as a unique input. Reaching a segmentation accuracy of 96.3%, recall of 78.2%, and precision of 85.5% on Mtb detection for the model trained with a mix of manually and synthetically annotated databases. The experiments validate as well the synthetic label generation technique, which provides more conscience and accurate results than the manually segmented techniques. The mask generation workflow opens new opportunities for the microbial AI development field. The system implemented may change the paradigm in preclinical anti-mycobacteria drug development for since enables the adoption of TLM, a technology that provides extremely valuable information on drug efficacy but is seldom leveraged due to the complex image processing it involves. • A semi-supervised Deep Learning U-Net based model for TLM bacterial culture analysis. • The method reports bacterial growth and death with PhC as a single input. • Evaluation in two different Mycobacterium ( M. tuberculosis and M. smegmatis ). • Creating a methodology for synthetic annotation of the bacterial population in TLM. • Comparative analysis of manual and synthetic databases.