An algorithm for detection of tuberculosis bacilli in Ziehl-Neelsen sputum smear images

This work proposes an algorithm oriented to the detection of tuberculosis bacilli in digital images of sputum samples, inked with the Ziehl Neelsen method and prepared with the direct, pellet and diluted pellet methods. The algorithm aims at automating the optical analysis of bacilli count and the calculation of the concentration level. Several algorithms have been proposed in the literature with the same objective, however, in no case is the performance in sensitivity and specificity evaluated for the 3 preparation methods. The proposed algorithm improves the contrast of the colors of interest, then thresholds the image and segments by labeling the objects of interest (bacilli). Each object then has its geometrical descriptors and photometric descriptors. With all this, a characteristic vector is formed, which are used in the training and classification process of an SVM. For the training 225 images obtained by the 3 preparation methods were used. The proposed algorithm reached, for the direct method, a sensitivity level of 93.67% and a specificity level of 89.23%. In the case of the Pellet method, a sensitivity of 92.13% and a specificity of 82.58% was obtained, while for diluted Pellet the sensitivity was 92.81% and the specificity 83.61%

Automated methods for tuberculosis detection/diagnosis : a literature review

Funding: Welcome Trust Institutional Strategic Support fund of the University of St Andrews, grant code 204821/Z/16/Z.Tuberculosis (TB) is one of the leading infectious causes of death worldwide. The effective management and public health control of this disease depends on early detection and careful treatment monitoring. For many years, the microscopy-based analysis of sputum smears has been the most common method to detect and quantify Mycobacterium tuberculosis (Mtb) bacteria. Nonetheless, this form of analysis is a challenging procedure since sputum examination can only be reliably performed by trained personnel with rigorous quality control systems in place. Additionally, it is affected by subjective judgement. Furthermore, although fluorescence-based sample staining methods have made the procedure easier in recent years, the microscopic examination of sputum is a time-consuming operation. Over the past two decades, attempts have been made to automate this practice. Most approaches have focused on establishing an automated method of diagnosis, while others have centred on measuring the bacterial load or detecting and localising Mtb cells for further research on the phenotypic characteristics of their morphology. The literature has incorporated machine learning (ML) and computer vision approaches as part of the methodology to achieve these goals. In this review, we first gathered publicly available TB sputum smear microscopy image sets and analysed the disparities in these datasets. Thereafter, we analysed the most common evaluation metrics used to assess the efficacy of each method in its particular field. Finally, we generated comprehensive summaries of prior work on ML and deep learning (DL) methods for automated TB detection, including a review of their limitations.Publisher PDFPeer reviewe

Mycobacterium tuberculosis aggregates affect the early macrophage response to infection and are detectable in human lung tissue

Mycobacterium tuberculosis (Mtb) can infect macrophages as single or aggregated bacilli, where aggregate infection of macrophages was shown to have a substantially higher probability to result in macrophage death. Given that the response of macrophages to Mtb infection may determine the infection trajectory, it is important to understand the macrophage response to infection with Mtb aggregates. Here I investigated the early transcriptional response of monocyte derived macrophages (MDMs) to Mtb aggregate infection. I found that Mtb aggregates elicited the highest TNF-α and pro-inflammatory response relative to single Mtb bacilli. Additionally, aggregate-mediated MDM death was dependent on infection with live Mtb aggregates. I also investigated macrophage acidification in response to infection with Mtb aggregates and found that acidification, per Mtb bacillus, decreased as aggregate size increased. This suggests that Mtb aggregates have an advantage over single bacilli due to a weaker host response per mycobacterium. I also quantified Mtb aggregate number in human lung tissue sections using custom digital image analysis pipelines and developed a convolutional neural network (CNN) model, HyRoNet, to automate and expand the analysis. I found that Mtb aggregates occurred often, but not exclusively, in association with the granulomatous cavity surface. Together, these observations suggest a potentially important role for Mtb aggregation in the pathogenesis of Mtb

Image segmentation and object classification for automatic detection of tuberculosis in sputum smears

Includes bibliographical references (leaves 95-101).An automated microscope is being developed in the MRC/UCT Medical Imaging Research Unit at the University of Cape Town in an effort to ease the workload of laboratory technicians screening sputum smears for tuberculosis (TB), in order to improve screening in countries with a heavy burden of TB. As a step in the development of such a microscope, the project described here was concerned with the extraction and identification of TB bacilli in digital images of sputum smears obtained with a microscope. The investigations were carried out on Ziehl-Neelsen (ZN) stained sputum smears. Different image segmentation methods were compared and object classification was implemented using various two-class classifiers, for images obtained using a microscope with 100x objective lens magnification. The bacillus identification route established for the 100x images, was applied to images obtained using a microscope with 20x objective lens magnification. In addition, one-class classification was applied the 100x images. A combination of pixel classifiers performed best in image segmentation to extract objects of interest. For 100x images, the product of the Bayes’, quadratic and logistic linear classifiers resulted in a percentage of correctly classified bacillus pixels of 89.38%; 39.52% of pixels were incorrectly classified. The segmentation method did not miss any bacillus objects with their length in the focal plane of an image. The biggest source of error for the segmentation method was staining inconsistencies. The pixel segmentation method performed poorly on images with 20x magnification. Geometric change invariant features were extracted to describe segmented objects; Fourier coefficients, moment invariant features and colour features were used. All two-class object classifiers had balanced performance for 100x images, with sensitivity and specificity above 95% for the detection of an individual bacillus after Fisher mapping of the feature set. Object classification on images with 20x magnification performed similarly. One-class object classification using the mixture of Gaussians classifier, without Fisher mapping of features, produced sensitivity and specificity above 90% when applied to 100x images

Tuberculosis bacteria detection and counting in fluorescence microscopy images using a multi-stage deep learning pipeline

The manual observation of sputum smears by fluorescence microscopy for the diagnosis and treatment monitoring of patients with tuberculosis (TB) is a laborious and subjective task. In this work, we introduce an automatic pipeline which employs a novel deep learning-based approach to rapidly detect Mycobacterium tuberculosis (Mtb) organisms in sputum samples and thus quantify the burden of the disease. Fluorescence microscopy images are used as input in a series of networks, which ultimately produces a final count of present bacteria more quickly and consistently than manual analysis by healthcare workers. The pipeline consists of four stages: annotation by cycle-consistent generative adversarial networks (GANs), extraction of salient image patches, classification of the extracted patches, and finally, regression to yield the final bacteria count. We empirically evaluate the individual stages of the pipeline as well as perform a unified evaluation on previously unseen data that were given ground-truth labels by an experienced microscopist. We show that with no human intervention, the pipeline can provide the bacterial count for a sample of images with an error of less than 5%.Publisher PDFPeer reviewe

Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research

Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO’s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%

Hardware and software integration and testing for the automation of bright-field microscopy for tuberculosis detection

Automated microscopy for the detection of tuberculosis (TB) in sputum smears would reduce the load on technicians, especially in countries with a high TB burden. This dissertation reports on the development and testing of an automated system built around a conventional microscope for the detection of TB in Ziehl-Neelsen (ZN) stained sputum smears. Microscope auto-focusing, image analysis and stage movement were integrated. Images were captured at 40x magnification

Targeted computational analysis of the C3HEB/FEJ mouse model for drug efficacy testing

2020 Spring.Includes bibliographical references.Efforts to develop effective and safe drugs for the treatment of tuberculosis (TB) require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology, therefore, has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models. The model approach also has broader applications to other diseases and tissues. This also includes animals that are undergoing anti-mycobacterial treatment and host immune system modulation. A complimentary software package called 'Mycobacterial Image Analysis' (MIA) had also been developed that characterizes the varying bacilli characteristics such as density, aggregate/planktonic bacilli size, fluorescent intensity, and total counts. This further groups the bacilli characteristic data depending on the seven different classifications that are selected by the user. Using this approach allows for an even more targeted analysis approach that can determine how therapy and microenvironments influence the Mtb response

Developing Zebrafish embryos as a model to study host-material Interactions and wound healing

Inappropriate wound healing represents a considerable medical challenge associated with high mortality. However, improving on current wound healing therapies has proven difficult due to the complex and dynamic wound environment. The complexity of the wound healing process also puts high demands on the animal models used in wound research, since ideally such models should encompass the full complexity of the wound healing process, and at the same time be accessible for advanced biomedical analysis methods. In this thesis, the aim was to further develop the use of zebrafish embryos in wound healing research. Key advantages of zebrafish embryo models are the ability to visualize complex biological processes in high detail in intact tissues, as well as highly tractable genetics. The first part of the work describes the development of a zebrafish embryo model for investigating the immunomodulatory properties of hydrogels derived from decellularized extracellular matrix (ECM). The results demonstrate that the hydrogels can be properly injected into the embryos and that the host-materials interactions can be explored in detail inside live zebrafish embryos during wound healing. This constitutes a new in vivo model for investigating immunomodulatory materials in a realistic wound healing context. The second part of the work describes the development of a confocal Raman spectrometry imaging (cRSI) method for biomolecular characterization and the study of biological processes in zebrafish. This represents a new imaging modality that enables simultaneous inspection of a multitude of biomolecules in a label-free manner. The use of cRSI was demonstrated for biomolecular discrimination of mycobacteria in a zebrafish infection model, and for live in vivo imaging of zebrafish during the early wound response. Taken together, the work in this thesis has provided a new methodologies and insight for the use in zebrafish embryo models in wound healing research.Open Acces