Lung Segmentation
Lung Segmentation - Related Videos
07:06
A Simple Method of Mouse Lung Intubation
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Cited by 50 •
2013
This paper describes a striaghforward and efficient method of intubating mice for pulmonary function measurements or pulmonary instillation, that allows the mice to recover and be studied at later times. The procedure involves an inexpensive fiberoptic light source that directly illuminates the trachea.
07:54
In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
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Cited by 16 •
2012
Simple and reproducible procedures are described for making three structurally distinct collagen assemblies from a common commercially available Type I collagen monomer. Native type, fibrous long spacing or segmental long spacing collagen can be constructed by varying the conditions to which the 300 nm long and 1.4 nm diameter monomer building block is exposed.
01:47
Lung Capacity
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2019
The air in the lungs is measured in volumes and capacities. Lung volume measures reflect the amount of air taken in, released, or left over after a lung function, like a single inhalation. Lung capacity measures are sums of two or more lung volume measures. There are four defined lung volume measures: tidal volume, inspiratory reserve volume, expiratory reserve volume, and residual volume. Tidal volume is the amount of air inhaled and exhaled in a normal breath. Inspiratory...
05:35
Lung Instillation and Fixation: Introducing Liquid into the Trachea and Fixing Lungs to Measure the Fixed Lung Volume
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2023
This video describes a simple and reliable method of introducing liquid into the trachea for lung instillation, fixation, and lung volume measurement.
03:24
Anterior Eye Segment Dissection: A Method to Isolate Cornea Bearing Anterior Segment from Porcine Eye
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2025
In this video, we demonstrate the dissection of the cornea-bearing anterior segment of the porcine eye. The isolated segment can be used for organ culturing to study the pathophysiology of eye disorders.
FAQs
The process relies on differences in intensity, texture, and shape between lung regions and surrounding structures. These features provide complementary evidence: intensity separates regions with different image values, texture captures local appearance, and shape helps identify anatomically consistent boundaries. Combining these cues supports more precise delineation than relying on a single image characteristic alone.
Initial identification of lung regions does not by itself provide the precise anatomical boundaries needed for analysis. Refining the borders converts an approximate region into a more exact representation of the lung area. This precision matters because downstream measurements, such as lung volume and the extent of abnormalities, depend on how accurately the boundaries are established.
Both approaches can be used to identify lung regions in computed tomography or radiographic images, but they represent different tool categories within the same workflow. Image-processing methods analyze image characteristics directly, whereas machine-learning models use learned patterns to support region identification. Their role is to transform complex medical images into boundaries suitable for quantitative clinical analysis.
A typical workflow begins with a computed tomography or radiographic image and examines differences in intensity, texture, and shape across the image. The selected method then identifies likely lung regions and refines their borders against surrounding structures. The resulting delineated regions provide the anatomical representation required for measurements and further assessment of lung abnormalities.
Segmentation places abnormalities within measurable lung anatomy rather than leaving them as features in an otherwise complex image. By delineating the relevant lung regions, clinicians and researchers can perform quantitative assessment of lung volume and examine abnormalities such as tumors, nodules, and infection-related changes. This supports structured analysis of findings across medical images.
Its clinical value extends across diagnosis, disease monitoring, surgical planning, and radiation therapy. The technique supplies measurable anatomical information that can help characterize lung findings, follow changes over time, and define relevant anatomy before treatment or intervention. Because each use depends on image-based boundaries, accurate segmentation connects medical imaging with practical clinical decisions.