ArticleJournal of medical imaging (Bellingham, Wash.)2017
Ziehl-Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 61 citations in OpenAlex.
- A comprehensive raw dataset of Ziehl-Neelsen-stained sputum smear microscopy images forData in brief · 2026Article
- AutomatedInternational journal of biomedical imaging · 2026Article
- Tuberculosis Today: Microbial Insights, Epidemiological Trends, and the Role of Molecular Diagnostics.Pathogens (Basel, Switzerland) · 2025Review
- Image database with slides prepared by the Ziehl-Neelsen method for training automated detection and counting systems for tuberculosis bacilli.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Advancing the fight against tuberculosis: integrating innovation and public health in diagnosis, treatment, vaccine development, and implementation science.Frontiers in medicine · 2025Review
- Evolution of Laboratory Diagnosis of Tuberculosis.Clinics and practice · 2024Review
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- Review
- Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.Diagnostics (Basel, Switzerland) · 2023Review
- A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images.Diagnostics (Basel, Switzerland) · 2022Review
- Tuberculosis diagnostics: overcoming ancient challenges with modern solutions.Emerging topics in life sciences · 2020Review
- A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions.Journal of imaging · 2020Review
- Image processing for AFB segmentation in bacilloscopies of pulmonary tuberculosis diagnosis.PloS one · 2019Article
- Ziehl-Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis.Journal of medical imaging (Bellingham, Wash.) · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ziehl-Neelsen stained microscopy is a crucial bacteriological test for tuberculosis detection, but its sensitivity is poor. According to the World Health Organization (WHO) recommendation, 300 viewfields should be analyzed to augment sensitivity, but only a few viewfields are examined due to patient load. Therefore, tuberculosis diagnosis through automated capture of the focused image (autofocusing), stitching of viewfields to form mosaics (autostitching), and automatic bacilli segmentation (grading) can significantly improve the sensitivity. However, the lack of unified datasets impedes the development of robust algorithms in these three domains. Therefore, the Ziehl-Neelsen sputum smear microscopy image database (ZNSM iDB) has been developed, and is freely available. This database contains seven categories of diverse datasets acquired from three different bright-field microscopes. Datasets related to autofocusing, autostitching, and manually segmenting bacilli can be used for developing algorithms, whereas the other four datasets are provided to streamline the sensitivity and specificity. All three categories of datasets were validated using different automated algorithms. As images available in this database have distinctive presentations with high noise and artifacts, this referral resource can also be used for the validation of robust detection algorithms. The ZNSM-iDB also assists for the development of methods in automated microscopy.
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Registered trials
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