The lungs are not simply divided into two or three parts—they are made up of ten bronchopulmonary segments on each side, which function as separate functional units. This is one of the little-known anatomical facts vital to diagnostic imaging.
Each segment is independently supplied with blood, ventilated, and drained of lymph. This allows the clinician to accurately identify lesions, plan treatment, and perform surgical interventions with high precision.
Mastering the lung segments is a foundational knowledge and an essential professional competency in modern radiology practice.
Check our guide on "internal lung mapping" for foundational lung structure.
Lung segmentation improves accuracy in diagnosing lung disease.
What Is Lung Segmentation in Radiology?
Lung segmentation in radiology is a fundamental step in deciphering respiratory abnormalities. It is not only the process of separating anatomical regions, but also the art of accurately identifying each microstructure within a living system. From an imaging perspective, each segment represents an independent functional unit, associated with distinct ventilation and perfusion – and therefore, has a distinct clinical diagnostic significance.
Precise definition
Lung segmentation divides the lung into bronchopulmonary segments based on the branching of the secondary bronchial tree. Each segment is a cone-shaped region with its apex pointing toward the hilum, surrounded by thin connective tissue.
Clinical objectives
Segmentation helps the physician locate lesions in specific anatomical units, improving the accuracy of imaging reports. It is the basis for analyzing tumors, inflammation, collapse, or focal lesions.
Modern imaging applications
In radiology practice, segmentation is strongly applied to CT, HRCT, and chest MRI. This supports detailed analysis of each segment and maps lesions with high reliability.
Overview of Bronchopulmonary Segments
The bronchopulmonary segments are the most important anatomical-functional units in the lung's structure. Each segment is supplied by a second-order bronchus and accompanied by a separate vascular system. This arrangement allows each segment to function independently and is the basis for determining the lesion boundaries, guiding intervention, and parenchymal-sparing surgery.
Segmental structure of the right lung
The right lung consists of 10 segments:
- Upper lobe: apical (S1), posterior (S2), anterior (S3)
- Middle lobe: lateral (S4), medial (S5)
- Lower lobe: superior (S6), medial basal (S7), anterior basal (S8), lateral basal (S9), posterior basal (S10)
Segmental structure of the left lung
The left lung also consists of 10 segments (depending on the document, S7-8 can be combined):
- Upper lobe: apicoposterior (S1+S2), anterior (S3), superior lingular (S4), inferior lingular (S5)
- Lower lobe: superior (S6), anteromedial basal (S7+S8), lateral basal (S9), posterior basal (S10)
Deep clinical significance
Understanding the location and names of the segments helps doctors describe the lesion accurately in the CT report chest, especially when monitoring lung nodules, abscesses, or the spread of inflammation.
Radiopaedia Classification of Lung Segments
Radiopaedia provides a standardized lung segmentation system, based on the anatomical branching of the secondary bronchi. The system is built on a rich imaging database and is highly applicable in clinical practice. With its precise segment identification and spatial arrangement, Radiopaedia has become the leading reference for chest imaging professionals worldwide.
Classification principles
Radiopaedia divides the lung into 10 segments for each side based on the division from the lobar bronchi. Each segment has its code, usually S1 to S10, combined with the traditional Latin name to support international comparison.
Imaging strengths
The system is accompanied by multiplanar illustrations from CT and HRCT, providing sharp visual maps. Each segment is specifically located on axial, coronal, and sagittal slices.
Diagnostic applications
Radiopaedia helps identify lung lesions with millimeter accuracy, especially in assessing tumors, localized inflammation, or planning endoscopic lung segment resection. It is an important tool for standardizing imaging reports according to global guidelines.
How Segmentation Helps in Diagnosing Lung Disease
Lung segmentation is a strategic diagnostic tool in respiratory imaging. Localizing lesions to specific anatomic units allows the clinician to analyze the cause, nature, and extent of disease accurately. Pulmonary segmentation has become the cornerstone of clinically oriented chest CT reporting, from localized inflammation to diffuse malignancy.
Precise lesion localization
When nodules, atelectasis, or inflammatory foci are present, segmentation allows the clinician to describe the lesion in three dimensions – for example, "opaque nodules in the S6 segment of the right lung," rather than vaguely describing the area.
Differentiating diffuse from localized lesions
Segmentation allows for the assessment of clear boundaries between areas of lesions. Lesions confined to one segment often suggest local causes such as foreign bodies or bronchial stenosis. In contrast, multisegmental lesions suggest a more extensive process, such as tuberculosis or disseminated pneumonia.
Instructions for intervention and follow-up
Segmentation assists in indicating transmural biopsy, drainage, or endoscopic segmentectomy. It also helps compare images over multiple scans to assess disease progression more accurately.
CT and HRCT Scans in Segment Identification
CT and HRCT are the leading tools for segmental identification of the lung with superior spatial resolution. Thanks to the ability to reconstruct multiple planes, segmental structures – individual bronchial branches, blood vessels, and tissue boundaries – are exposed. Segmental lesion localization using slice images has become the standard in modern clinical practice and respiratory research.
Multislice chest CT – spatial localization tool
Multislice CT (MDCT) provides a series of thin slices (1-2 mm), allowing the clinician to view the segment as a three-dimensional anatomical map. Axial, coronal, and sagittal views help to determine exactly which segment the lesion is located in.
HRCT – detailed parenchymal extraction
HRCT (High-Resolution CT) is particularly useful in evaluating interstitial diseases. Segments with interstitial lesions, fibrosis, or bronchiectasis are visualized, thereby supporting the classification of ILD according to the lesion pattern.
Integration of 3D reconstruction software
Many centers use 3D reconstruction software to highlight each segment. This supports accurate surgical planning and clear presentation of the lesion in multidisciplinary consultations.
Challenges and Common Errors in Segment Interpretation
Interpreting lung segments in imaging requires high accuracy, a deep understanding of anatomy, and a flexible spatial orientation. Errors in segment identification can directly affect diagnostic decisions, treatment, and surgical interventions. The complexity of the anatomical distribution, especially at the interface between segments, is the cause of many clinical challenges.
Confusion between adjacent segments
One of the common errors is confusion between S1 and S2 in the upper lobe or between S6 and S10 in the lower lobe. The similarity in location can be confusing, especially on the axial view, when the lesion is located near the interlobular fissure.
Influence of anatomical variations
Some patients have segmental variations such as S7–S8 fusion or bronchovascular deviation, leading to confusion in localization. Failure to detect these variations may result in misleading lesion reporting.
Imaging limitations within the pathologic parenchyma
Diffuse lesions, atelectasis, or pleural effusions may obscure important anatomic landmarks. In these cases, segmentation should be inferred from the associated bronchi or vessels, rather than relying solely on parenchymal images.
Human factors and clinical experience
Interpretation depends on experience with the film, multiplanar reconstruction skills, and deep anatomic knowledge. Early training in clear segmentation orientation is critical to long-term accuracy.
Conclusion
Lung segmentation is the key to accurately understanding the anatomical map inside the thorax. Each segment represents an independent functional unit, with clear directional value in diagnosing and treating respiratory diseases.
Applying CT and HRCT in segmentation helps doctors see high-resolution lesions, reproducing three-dimensional space sharply. Radiopaedia provides a standardized classification system, supporting global image reporting synchronization.
By mastering the segmentation, doctors can read the film accurately and predict the pathology, optimize interventions, and individualize treatment for each patient.
Frequently Asked Questions (FAQs)
- Are the lung segments the same in both lungs? – Lung segmental structures are similar in principle but differ in detail between the two. The right lung has three lobes and ten distinct segments, while the left lung typically has a lingular and basal fusion.
- Is Radiopaedia an authoritative reference? – Radiopaedia is a widely recognized medical imaging platform whose content is reviewed by global experts. Its segmentation system is highly standardized and has practical clinical value.
- How do CT and HRCT differ in segment identification? – CT provides a global anatomical image, while HRCT emphasizes parenchymal detail. Combining both techniques allows for accurate segment identification in conditions ranging from pulmonary nodules to interstitial fibrosis.
- What are common errors when reading lung segments? – Confusion between adjacent segments, failure to recognize anatomic variations, and misjudging the location of lesions due to obscured pathological parenchyma are the three most common errors in practice.
- Can lung segments be identified using plain radiography? – Standard chest radiography provides only a general view of the lung and insufficient resolution to accurately delineate individual segments. Segment identification requires cross-sectional imaging such as CT or HRCT.
