Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape.

Screening for tuberculosis (TB) in low- and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.

Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears

Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods

Microscopy is a rapid diagnosis method for many infectious diseases like tuberculosis (TB). In TB bacilli identification, specimens are stained using Ziehl–Neelsen or Auramine dye and are examined by technicians thoroughly for any infectious microbes. For pathological study, the images of these microbes are captured using microscopes and image processing is applied for further analysis. However, choosing 100 field of views (FOV) randomly from a 2 × 1 cm square area of sputum specimen may lead to inconsistency in specificity. The examination of specimens is a tedious process, and it requires especially skilled technicians for screening the sputum smear samples. The proposed tuberculosis detection system consists of two subsystems—a data acquisition system and a recognition system. In the data acquisition system, a motorized microscopic stage is designed and developed to automate the acquisition of all FOVs. Here the microscopic stage movement is motorized and scanning patterns are defined by the user for specimen examination. After the acquisition of all FOVs, data are passed to the recognition system. In the recognition system, transfer learning method is implemented by customizing the Inception V3 DeepNet model. This model learns from the pre-trained weights of Inception V3 and classifies the data using support vector machine (SVM) from the transferred knowledge. For training and testing the customized Inception V3 model, a public TB dataset (Shah et al. in J Med Imaging 4(2):027503, 2017. https://doi.org/10.1117/1.jmi.4.2.027503
 ) and our own acquired microscopic digital dataset are used for analysis. In this model, the fixed feature representations are taken from the top stack layer of Inception V3 DeepNet and are classified using SVM. This model attains an accuracy of 95.05%, thereby reducing the dependency on skilled technicians in the screening process and increasing sensitivity and specificity.

Tuberculosis (TB) detection system using deep neural networks

In low-resource areas, the most common method of tuberculosis (TB) diagnosis is visual identification of rod-shaped TB bacilli in microscopic images of sputum smears. We present an algorithm for automated TB detection using images from digital microscopes such as CellScope, a novel, portable device capable of brightfield and fluorescence microscopy. Automated processing on such platforms could save lives by bringing healthcare to rural areas with limited access to laboratory-based diagnostics. Our algorithm applies morphological operations and template matching with a Gaussian kernel to identify candidate TB-objects. We characterize these objects using Hu moments, geometric and photometric features, and histograms of oriented gradients and then perform support vector machine classification. We test our algorithm on a large set of CellScope images (594 images corresponding to 290 patients) from sputum smears collected at clinics in Uganda. Our object-level classification performance is highly accurate, with average precision of 89.2% +/- 2.1%. For slide-level classification, our algorithm performs at the level of human readers, demonstrating the potential for making a significant impact on global healthcare.

/pdf/automated-tuberculosis-diagnosis-using-fluorescence-images-90k3zvx5c5.pdf

Automated Tuberculosis Diagnosis Using Fluorescence Images from a Mobile Microscope

Worldwide, laboratory technicians tediously read sputum smears for tuberculosis (TB) diagnosis. We demonstrate proof of principle of an innovative computational algorithm that successfully recognizes Ziehl-Neelsen (ZN) stained acid-fast bacilli (AFB) in digital images. Automated, multi-stage, color-based Bayesian segmentation identified possible 'TB objects', removed artifacts by shape comparison and color-labeled objects as 'definite', 'possible' or 'non-TB', bypassing photomicrographic calibration. Superimposed AFB clusters, extreme stain variation and low depth of field were challenges. Our novel method facilitates electronic diagnosis of TB, permitting wider application in developing countries where fluorescent microscopy is currently inaccessible and unaffordable. We plan refinement and validation in the future.

/pdf/image-processing-techniques-for-identifying-mycobacterium-1vhg37gf9h.pdf

Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains.

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Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains.