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

Analysis of Tuberculosis using Smear Image

An automatic method for the detection of Tuberculosis (TB) bacilli from microscopic sputum smear images is presented in this paper. According to WHO, TB is the ninth leading cause of death all over the world. There are various techniques to diagnose TB, of which conventional microscopic sputum smear examination is considered. However, the mentioned method of diagnosis is time intensive and error prone, even in experienced hands. The proposed method performs detection of TB, by image binarization and subsequent classification of detected regions using a convolutional neural network. We have evaluated our gist algorithm using a dataset of sputum smear microscopic images with different backgrounds (high density and low-density images). Experimental results show that the proposed algorithm achieves for the TB detection. The proposed method automatically detects whether the sputum smear images is infected with TB or not. This method will aid clinicians to predict the disease accurately in a short span of time, thereby helping in improving the clinical outcome

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

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

Towards fully automated analysis of sputum smear microscopy images

Sputum smear microscopy is used for diagnosis and treatment monitoring of pulmonary tuberculosis (TB). Automation of image analysis can make this technique less laborious and more consistent. This research employs artificial intelligence to improve automation of Mycobacterium tuberculosis (Mtb) cell detection, bacterial load quantification, and phenotyping from fluorescence microscopy images. I first introduce a non-learning, computer vision (CV) approach for bacteria detection, employing ridge-based approach using the Hessian matrix to detect ridges of Mtb bacteria, complemented by geometric analysis. The effectiveness of this approach is assessed through a custom metric using the Hu moment vector. Results demonstrate lower performance relative to literature metrics, motivating the need for deep learning (DL) to capture bacterial morphology. Subsequently, I develop an automated pipeline for detection, classification, and counting of bacteria using DL techniques. Firstly, Cycle-GANs transfer labels from labelled to unlabeled fields of view (FOVs). Pre-trained DL models are used for subsequent classification and regression tasks. An ablation study confirms pipeline efficacy, with a count error within 5%. For downstream analysis, microscopy slides are divided into tiles, each of which is sequentially cropped and magnified. A subsequent filtering stage eliminates non-salient FOVs by applying pre-trained DL models along with a novel method that employs dual convolutional neural network (CNN)-based encoders for feature extraction: one encoder is dedicated to learning bacterial appearance, and the other focuses on bacterial shape, which both precede into a bottleneck of a smaller CNN classifier network. The proposed model outperforms others in accuracy, yields no false positives, and excels across decision thresholds. Mtb cell lipid content and length may be related to antibiotic tolerance, underscoring the need to locate bacteria within paired FOV images stained with distinct cell identification and lipid detection, and to measure bacterial dimensions. I employ a proposed UNet-like model for precise bacterial localization. By combining CNNs and feature descriptors, my method automates reporting of both lipid content and cell length. Application of the approaches described here may assist clinical TB care and therapeutics research

Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy

Includes abstract.Includes bibliographical references (leaves 140-144).Automated microscopy for the detection of tuberculosis (TB) in sputum smears seeks to address the strain on technicians in busy TB laboratories and to achieve faster diagnosis in countries with a heavy TB burden. As a step in the development of an automated microscope, the project described here was concerned with microscope auto-positioning; this primarily involves generating a point of reference on a slide, which can be used to automatically bring desired fields on the slide to the field-of-view of the microscope for re-examination. The study was carried out using a conventional microscope and Ziehl- Neelsen (ZN) stained sputum smear slides. All images were captured at 40x magnification. A digital replication, the virtual slide map, of an actual slide was constructed by combining the manually acquired images of the different fields of the slide. The geometric hashing scheme was found to be suitable for auto-stitching a large number of images (over 300 images) to form a virtual slide map. An object recognition algorithm, which was also based on the geometric hashing technique, was used to localise a query image (the current field-of-view) on the virtual slide map. This localised field-of-view then served as the point of reference. The true positive (correct localisation of a query image on the virtual slide map) rate achieved by the algorithm was above 88% even for noisy query images captured at slide orientations up to 26°. The image registration error, computed as the average mean square error, was less than 14 pixel2 (corresponding to 1.02 μm2 and 0.001% error in an image measuring 1030 x 1300 pixels) corresponding to a root mean square registration error of 3.7 pixels. Superior image registration accuracy was obtained at the expense of time using the scale invariant feature transform (SIFT), with a image registration error of 1 pixel2 (0.07 μm2). The object recognition algorithm is inherently robust to changes in slide orientation and placement, which are likely to occur in practice as it is impossible to place the slide in exactly the same position on the microscope at different times. Moreover, the algorithm showed high tolerance to illumination changes and robustness to noise

Gold nanoparticles-based sensors for detection of mycobacterium tuberculosis genomic DNA

Tuberculosis (TB) caused by Mycobacterium tuberculosis (MTB), is an airborne disease that strikes one third of the globe’s population. In addition to infection of 9.6 million patients, TB claimed the lives of 1.5 million people in 2014 only. The majority of TB patients are present in the third world where the balance between cost-effective diagnostic method and prevalence of TB is difficult to achieve. Accurate diagnosis of TB is necessary to timely initiation of treatment. The available diagnostic tools are slow, while the rapid methods are either inaccurate or relatively unaffordable. So, sometimes the diagnosis is presumptive based on the clinical findings and the treatment is empiric. The treatment is lengthy and demands the administration of multiple antibiotics. However, the emergence of drug resistance threatened the global control programs of TB. The objective of this work is to develop cheap, fast and accurate detection methods. Two gold nanoparticles (AuNPs) based sensors were developed for colorimetric and fluorometric detection of MTB. Seventy two anonymous sputum samples were cultured then DNA was extracted. MTB H37Ra was the positive control while M. smegmatis and 8 non-MTB and negative controls. Characterization of the samples was achieved by multiplex PCR using MTB and NTM specific primers. Random samples were amplified by 16S-23S ITS primers and sequenced. Drug resistance associated mutations of MDR-TB were identified by MAS-PCR. The colorimetric assay aim was the detection of amplified MTB DNA by cationic AuNPs. The samples were amplified by IS6110 and rpoB primers. Only MTB samples yielded amplicons. So the negatively charged dsDNA attracted the positively charged AuNPs inducing their aggregation and the color turned blue. While the negative samples did not yield any amplicons and the AuNPs remained dispersed so the color was red. The sensitivity and specificity was 100% and the detection limit was 5.4 ng/μl of MTB DNA. The fluorometric assay exploited the quenching property of 40 nm AuNPs. The unamplified DNA was fragmented in the presence of 16s rDNA specific probe tagged with the fluorophore CY-3 by sonication and denatured for 3 min at 95 ºC followed by annealing at 52ºC for 45 sec. Then AuNPs were added and the fluorescence was measured. By FRET, the relative fluorescence was calculated revealing a cut-off value of 3. In MTB samples, the CY3-16s rDNA specific probe hybridized with its target and became spaced from the AuNPs allowing high fluorescence to be detected. Due to the lack of target-probe hybridization in the negative samples, the AuNPs were adsorbed on the probe and thus the fluorescence is quenched. Thirteen samples were chosen randomly, amplified and sequenced. Sequencing confirmed that 12/13 samples were MTB with 100% concordance with the multiplex PCR and FRET. The assay had sensitivity and specificity of 98.6% and 90% respectively and concordance of 98% with multiplex PCR. The detection limited was calculated to be 10 ng/ul. In conclusion, two AuNPs based sensors were developed to allow low cost and rapid detection of MTB on low source settings. The assays are rapid, sensitive and can have great potential in clinical practice for TB diagnosis

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications

Tuberculosis in Children: Diagnosis and Epidemiology

Globally, an estimated one-million new tuberculosis (TB) cases occurred in children in 2014. For a long time, research of TB in children has been neglected. Most research and surveillance of TB is performed in adults, and the resulting lack of evidence in children is a major barrier for implementation of rational control strategies for children, including diagnosis. More research on TB in children is of importance as children are more susceptible to developing severe extrapulmonary TB, children require different approaches to both diagnosis and treatment and paediatric TB reflects the ongoing transmission of TB in the population. This research gap on the epidemiology of tuberculosis in children, especially in high burden countries, should be addressed in order to better understand the dynamics of TB transmission in both adults and children. Accurate data are the basis for establishment of effective control strategies. This thesis aims to assess the diagnostic role of microscopic observation drug susceptibility (MODS) and mycobacterial blood culture for diagnosis of TB in children as well as to present the epidemiological characteristics of paediatric TB in northern Vietnam with regard to drug resistance and genotypes of Mycobacterium tuberculosis (MTB), the causative agent of TB, isolated from them. MODS is a low cost non-commercial liquid culture assay to detect MTB by microscopy. MODS was compared with conventional assays including Ziehl-Neelsen smear (ZN) and Löwenstein-Jensen culture (LJ) in a study conducted from 2009 to 2010 at the National Hospital for Paediatrics, a general paediatric hospital, in Hanoi, Vietnam. In suspected paediatric TB cases, the MODS test was shown to be significantly more sensitive than both smear (46.0% vs. 8.8%) and LJ (46.0% vs. 38.9%), and significantly faster than LJ with a median time difference of 28 days in favour of MODS (7 days vs. 35 days). The findings suggest that MODS is a rapid low-cost diagnostic tool for TB diagnosis in the paediatric population. The additional yield of mycobacterial blood culture was assessed in comparison to routine detection methods for TB diagnosis in children in two hospitals in Vietnam. The findings show that mycobacterial blood culture detected an additional six TB cases of which 5 cases were negative with other tests and in the remaining case no other tests were done. All six cases were susceptible to rifampicin and isoniazid. The overall performance of TB blood culture was poor as compared to routine culture with regard to detection rate (2.9%, 16/554 vs. 16.6%, 92/554) and turnaround time (26 days vs. 14 days). The incremental cost for adding one additional TB case is substantial. Therefore, addition of mycobacterial blood culture into routine diagnostics has limited utility in resource limited settings. To assess the molecular epidemiology of paediatric TB in northern Vietnam, a collection of 125 MTB isolates from children with TB admitted to NHP during 2009 to 2013 was analysed. Drug susceptibility testing results from 121 isolates and genotypes from 120 isolates were generated. The phenotypic drug susceptibility testing showed that 20.7% was resistant to isoniazid (25/121), 3.3% resistant to rifampicin (4/121), 28.1% resistant to streptomycin (34/121) and 2.5% resistant to ethambutol (1/121). There were 4 cases with multidrug resistant TB. The high frequency of resistance to isoniazid and streptomycin are consistent with data from adults in Vietnam, suggesting the ongoing transmission of drug resistant MTB in the community. MIRU typing patterns showed that the Beijing genotype was predominant in this population (63.3%, 76/120), which is in agreement with various prior studies in adults in Vietnam. These findings provide more evidence to support the hypothesis of the epidemic spread of the Beijing genotype in Vietnam. In this study, an association between Beijing genotype and drug resistance to streptomycin and isoniazid was observed. The number of MDR isolates was too small to draw conclusions regarding association of MDR and Beijing genotype. Collectively, these results demonstrate that liquid culture can improve the yield of TB diagnosis in Vietnam and mycobacterial blood culture should not be routinely performed for paediatric cases. The molecular epidemiology study also showed that the Beijing genotype is the predominant lineage among actively transmitted strains in Vietnam and that it is associated with both isoniazid and streptomycin resistance. Paediatric TB remains a significant cause of morbidity and mortality in Vietnamese children and sustained political and social commitment from all stakeholders, including governments, funders, academics and the medical community will be needed to improve diagnosis, treatment and prevention of TB in children globally