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Related Concept Videos

The Thoracic Cage: Ribs01:20

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Ribs are curved, flattened bones forming the thoracic cavity wall with the thoracic muscles. There are 12 pairs of thoracic ribs. The posterior ends of all the ribs articulate with the T1–T12 thoracic vertebrae. In contrast,the anterior ends of most ribs attach to the sternum via their costal cartilages.
Parts of a Typical Rib
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The Thoracic Cage: Sternum01:17

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The thoracic or rib cage forms the body's thorax (chest) portion. Its primary function in the body is to protect vital organs in the thoracic cavity, such as the heart and the lungs. It consists of 12 pairs of ribs with their costal cartilages and the sternum. The ribs are anchored posteriorly to the 12 thoracic vertebrae (T1-T12).
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Related Experiment Video

Updated: Apr 18, 2026

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
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Efficient ribcage segmentation from CT scans using shape features.

Ziyue Xu, Ulas Bagci, Colleen Jonsson

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary

    This study introduces an automated 3D algorithm for rib cage segmentation in CT scans. The novel method accurately separates the rib cage from other bone structures, achieving over 95% accuracy.

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    Area of Science:

    • Medical Imaging
    • Anatomical Analysis
    • Computational Anatomy

    Background:

    • Rib cage structure is crucial for chest CT scan analysis.
    • Accurate rib cage segmentation is challenging due to intensity variations and adjacent bone structures (shoulder blade, sternum).

    Purpose of the Study:

    • To develop a fully automated 3D algorithm for precise rib cage segmentation.
    • To address the challenge of separating the rib cage from other bone structures in CT images.

    Main Methods:

    • Automated segmentation of high-intensity bone structures.
    • Multi-scale Hessian analysis for plateness and vesselness feature extraction.
    • Detection and separation of non-rib cage bones using iterative relative fuzzy connectedness.

    Main Results:

    • The algorithm successfully segmented rib cages in 400 human CT scans and 100 small animal images.
    • Achieved a percent accuracy exceeding 95% for rib cage extraction.
    • Demonstrated robustness across various image resolutions.

    Conclusions:

    • The proposed automated 3D algorithm effectively segments the rib cage from complex anatomical structures.
    • This method offers a significant advancement for anatomical analysis of chest CT scans.
    • High accuracy and automation make it a valuable tool for medical imaging research.