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The respiratory zone of the human body, which stands in contrast to the conducting zone, comprises the structures that actively participate in the exchange of gases. The initiation of this zone is marked by the terminal bronchioles converging into respiratory bronchioles, the tiniest bronchiole classification. The respiratory bronchioles give way to the alveolar ducts that opens into a congregation of alveoli. Actively involved in gas exchange, alveoli resemble tiny sacs similar to clusters of...
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The upper respiratory tract plays a vital role in the respiratory system, comprising several structures that facilitate air intake and prepare air for the lungs. It also serves as the first line of defense against pathogens and particles. This tract includes the nose and nasal cavity, the oral cavity, the paranasal sinuses, and the pharynx, each with specific functions and features.
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Related Experiment Video

Updated: Jul 13, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Towards Connectivity-Aware Pulmonary Airway Segmentation.

Minghui Zhang, Yun Gu

    IEEE Journal of Biomedical and Health Informatics
    |October 12, 2023
    PubMed
    Summary

    This study introduces a novel framework to improve pulmonary airway segmentation for medical imaging. The new method effectively addresses broken airway predictions, enhancing accuracy for bronchoscopic procedures.

    Area of Science:

    • Medical Imaging Analysis
    • Computational Anatomy
    • Pulmonary Medicine

    Background:

    • Accurate pulmonary airway segmentation is crucial for clinical interventions and treating peripheral lung lesions.
    • Current Convolutional Neural Networks (CNNs) struggle with segmenting small, distal bronchi, leading to fragmented airways unsuitable for surgical guidance.

    Purpose of the Study:

    • To develop an improved segmentation framework addressing the limitations of CNNs in pulmonary airway analysis.
    • To enhance the connectivity and accuracy of segmented airways for clinical applications.

    Main Methods:

    • Proposed a connectivity-aware segmentation framework incorporating a Connectivity-Aware Surrogate (CAS) module to balance training.
    • Introduced a Local-Sensitive Distance (LSD) module to detect airway breakages and minimize distance map variations.

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    Main Results:

    • The framework demonstrated improved performance on public datasets (EXACT'09 and BAS).
    • Achieved high detection rates for airway branches (82.1%/96.5%) and length (79.6%/91.5%) on the datasets.
    • Significantly improved the connectedness of airway segmentation compared to standard CNN approaches.

    Conclusions:

    • The proposed connectivity-aware framework effectively enhances pulmonary airway segmentation accuracy and continuity.
    • This method offers a more reliable reference for bronchoscopic-assisted surgery and other clinical applications.