Extracting and classifying salient fields of view from microscopy slides of tuberculosis bacteria

Tuberculosis is one of the most serious infectious diseases, and its treatment is highly dependent on early detection. Microscopy-based analysis of sputum images for bacilli identification is a common technique used for both diagnosis and treatment monitoring. However, it [is] a challenging process since sputum analysis requires time and highly trained experts to avoid potentially fatal mistakes. Capturing fields of view (FOVs) from high resolution whole slide images is a laborious procedure, since they are manually localized and then examined to determine the presence of bacteria. In the present paper we propose a method that automates the process, thus greatly reducing the amount of human labour. In particular, we (i) describe an image processing based method for the extraction of a FOV representation which emphasises salient, bacterial content, while suppressing confounding visual information, and (ii) introduce a novel deep learning based architecture which learns from coarsely labelled FOV images and the corresponding binary masks, and then classifies novel FOV images as salient (bacteria containing) or not. Using a real-world data corpus, the proposed method is shown to out-perform 12 state of the art methods in the literature, achieving (i) an approximately 10% lower overall error rate than the next best model and (ii) perfect sensitivity (7% higher than the next best model).