Lesion Signal Detection
Lesion Signal Detection - Related Videos
02:42
RNA CISH Signal Detection: A Technique to Detect Chromogenic Signals During RNA In Situ Hybridization in Intact Tissue Specimens
0 Views •
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This video presents the protocol for signal detection during RNA chromogenic in situ hybridization. This technique can be used to diagnose an active oncogenic infection visually in an intact tissue specimen.
08:16
Comparative Lesions Analysis Through a Targeted Sequencing Approach
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This article describes a method to identify clonal and subclonal alterations among different specimens from a given patient. Although the experiments described here focus on a specific tumor type, the approach is broadly applicable to other solid tumors.
01:22
Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging to Visualize Multiple Sclerosis Lesions
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2025
Position the head of a patient with multiple sclerosis inside the radiofrequency or RF coil of an MRI machine.Begin fluid-attenuated inversion recovery or FLAIR imaging. Select a brain slice containing lesions near CSF-rich areas.The system generates a strong magnetic field, aligning protons in healthy and lesioned brain tissue and CSF along its direction, creating net magnetization.Apply an inversion RF pulse to flip the magnetization of all protons, aligning them opposite to the magnetic...
09:22
Sectioning Mammary Gland Whole Mounts for Lesion Identification
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Cited by 13 •
2017
We developed a method to successfully remove, process, section, and stain, for histopathological evaluation, mammary tissue that had originally been fixed on slides as whole mounts. This method may promote the collection and evaluation of mammary gland whole mounts in reproductive and developmental test guideline studies.
06:43
Quantitative Analysis and Characterization of Atherosclerotic Lesions in the Murine Aortic Sinus
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Cited by 51 •
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We describe procedures to quantify and characterize atherosclerotic lesions in mouse models using precision sectioning of the aortic sinus and ascending aorta combined with histochemical and immunohistochemical analysis.
FAQs
Reliable separation depends on more than a single bright or dark region. Lesion signal detection considers signal intensity together with spatial distribution and tissue contrast, then compares those features with surrounding normal anatomy. This combined view helps distinguish pathological patterns from imaging artifacts and natural anatomical variation, reducing the risk that an isolated signal difference is treated as a lesion.
Calibrated thresholds determine which signal values or patterns are treated as abnormal relative to the imaging data. If a threshold is poorly matched to normal variation, ordinary tissue differences or artifacts may be included; if it is too restrictive, relevant abnormalities may be missed. Calibration therefore influences how consistently detected regions represent the underlying tissue change.
Image processing and classification methods separate lesions from background information in different but complementary ways. Image processing emphasizes measurable features such as intensity, spatial distribution, and contrast, whereas classification organizes image patterns into categories that distinguish abnormal tissue from other signals. Either approach requires attention to surrounding anatomy so natural variation is not confused with pathology.
A lesion signal detection workflow begins by examining imaging data for differences in intensity, distribution, and contrast relative to nearby normal anatomy. Researchers can then apply calibrated thresholds, image-processing operations, or classification methods to separate candidate lesions from artifacts and variation. The resulting detection can be quantified by location and extent, producing measurements suitable for mapping and later comparison.
The most useful outputs are not limited to a yes-or-no detection. Lesion location and extent support lesion mapping and disease characterization, while the same measurements can inform treatment planning. Repeating these assessments provides a way to monitor progression or recovery, because changes in the detected region can be compared across observations.
In neuroscience, the location and extent of detected tissue damage provide a bridge between imaging and function. Researchers can relate lesion measurements to neural function, behavior, and clinical outcomes, helping characterize how structural changes correspond to observed consequences. This makes detection valuable not only for identifying abnormal tissue, but also for studying relationships between injury patterns and nervous-system performance.