Individual factors that lead to susceptibility to Mycobacterium tuberculosis infection among humans and animal models are not clearly defined. As a result, clinicians and scientists have little ability to diagnose and prognose the various clinical manifestations of tuberculosis, from life-long control of latent infection to active tuberculosis disease. Given the challenges in accurately predicting disease outcomes, vaccination with M. bovis Bacille Calmette-Guerin (BCG) vaccine is used globally in children to prevent systemic disease and tuberculous meningitis. However, in adults, epidemiological studies show variable protection ranging from 0% to 80%. As a part of a larger study to undercover the genomic and transcriptomic factors contributing to this variable efficacy, here we present a deep learning approach to identify mice which have been BCG-vaccinated from those that have not been vaccinated from hematoxylin and eosin stained lung sections of experimentally infected inbred mice. In a leave-one-out cross-validation of 59 slides, our method not only demonstrates ability to identify vaccinated mice with 93% accuracy and non-vaccinated mice with 100% accuracy. Through association with genomic and transcriptomic factors, we envision creating a blueprint for modifying and improving current vaccine strategies.
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.
A neutrophil is a type of white blood cell that is responsible for killing pathogenic bacteria but may simultaneously damage host tissue. We established a method to automatically detect neutrophils from slides stained with hematoxylin and eosin (H and E), because there is growing evidence that neutrophils, which respond to Mycobacterium tuberculosis, are cellular biomarkers of lung damage in tuberculosis. The proposed method relies on transfer learning to reuse features extracted from the activation of a deep convolutional network trained on a large dataset. We present a methodology to identify the correct tile size, magnification, and the number of tiles using multidimensional scaling to efficiently train the final layer of this pre-trained network. The method was trained on tiles acquired from 12 whole slide images, resulting in an average accuracy of 93.0%. The trained system successfully identified all neutrophil clusters on an independent dataset of 53 images. The method can be used to automatically, accurately, and efficiently count the number of neutrophil sites in regionsof-interest extracted from whole slide images.
Accurate detection and quantification of normal lung tissue in the context of Mycobacterium tuberculosis infection is of
interest from a biological perspective. The automatic detection and quantification of normal lung will allow the
biologists to focus more intensely on regions of interest within normal and infected tissues. We present a computational
framework to extract individual tissue sections from whole slide images having multiple tissue sections. It automatically
detects the background, red blood cells and handwritten digits to bring efficiency as well as accuracy in quantification of
tissue sections. For efficiency, we model our framework with logical and morphological operations as they can be
performed in linear time. We further divide these individual tissue sections into normal and infected areas using deep
neural network. The computational framework was trained on 60 whole slide images. The proposed computational
framework resulted in an overall accuracy of 99.2% when extracting individual tissue sections from 120 whole slide
images in the test dataset. The framework resulted in a relatively higher accuracy (99.7%) while classifying individual
lung sections into normal and infected areas. Our preliminary findings suggest that the proposed framework has good
agreement with biologists on how define normal and infected lung areas.
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