ArticleScientific reports2018
Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images.Yearbook of medical informatics · 2022Pooled it
- Lung image segmentation via generative adversarial networks.Frontiers in physiology · 2024Article
- Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC.Journal of ambient intelligence and humanized computing · 2023Article
- Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection.Frontiers in computational neuroscience · 2023Article
- Dynamic feature learning for COVID-19 segmentation and classification.Computers in biology and medicine · 2022Article
- MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images.Signal processing. Image communication · 2022Article
- MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification.Computers in biology and medicine · 2022Article
- Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow.Journal of digital imaging · 2021Article
- MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19.IEEE access : practical innovations, open solutions · 2021Article
- Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm.Pakistan journal of medical sciences · 2021Article
- ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans.PeerJ. Computer science · 2021Article
- PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines.Medical physics · 2020Article
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6 authors.
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Abstract
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.
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