Tissue Tracking Quality
Tissue Tracking Quality - Related Videos
08:19
Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo
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Cited by 11 •
2011
This article describes surface labeling and ex ovo tissue culture in the early chick embryo. Techniques amenable to time-lapse bright field, fluorescence, and optical coherence tomography imaging are presented. Tracking surface labels with high spatiotemporal resolution enables kinematic quantities such as morphogenetic strains (deformations) to be calculated in both two and three dimensions.
18:17
Obtaining High Quality RNA from Single Cell Populations in Human Postmortem Brain Tissue
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Cited by 39 •
2009
We describe a process using laser-capture microdissection to isolate and extract RNA from a homogeneous cell population, pyramidal neurons, in layer III of the superior temporal gyrus in postmortem human brains. We subsequently linearly amplify (T7-based) mRNA, and hybridize the sample to the Affymetrix human X3P microarray.
15:01
Tracking Neutrophil Intraluminal Crawling, Transendothelial Migration and Chemotaxis in Tissue by Intravital Video Microscopy
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Cited by 21 •
2011
We describe a protocol of brightfield intravital microscopy for measuring dynamic neutrophil-endothelial cell interactions during neutrophil recruitment in response to the source of a neutrophil chemoattractant in vivo. Neutrophil intraluminal crawling, transendothelial migration and chemotaxis in mouse cremaster muscle tissue are visualized with time-lapsed video photography and tracked with ImageJ.
09:03
Eye Tracking Young Children with Autism
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Cited by 93 •
2012
Eye tracking has long been used to study gaze patterns in typically-developing individuals, but recent technological advancements have made its use with clinical populations, including autism, more feasible. While eye-tracking young children with autism can offer insight into early symptom manifestations, it involves methodological challenges. Suggestions for best practices are provided.
07:36
C. elegans Tracking and Behavioral Measurement
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Cited by 14 •
2012
We have developed a video-rate tracking microscope system that can record and quantify C. elegans behavior at high resolution and high speeds. We have also developed computational methods to reduce the dimensionality of the worm images to a fundamental set of measurements that completely describe the shape of the worm.
FAQs
Tissue tracking quality depends on whether corresponding features remain identifiable from one image frame to the next. The analysis may follow landmarks, image intensity patterns, contours, or observed motion to estimate changes in position, shape, and movement. When correspondence is maintained, measurements across time are more dependable; when it is lost, apparent motion may reflect tracking error rather than true tissue behavior.
These image features provide different ways to maintain correspondence during tracking. Landmarks offer identifiable reference points, intensity describes patterns within the image, contours represent tissue boundaries, and motion supplies information about how structures change between frames. Their usefulness depends on the available image information, so assessing several feature types can help reveal whether the estimated tissue movement is consistent.
Deformation can alter a tissue's shape while it moves, making simple feature matching less reliable. Noise can obscure relevant image information, and changes in image quality can make a feature appear different even when the tissue remains corresponding. Accounting for these influences is essential because otherwise the analysis may introduce inaccurate motion, shape, or position estimates into later clinical interpretation.
Tracking assessment provides a way to examine whether tissue boundaries and features remain consistent across images. Inconsistencies can indicate errors in segmentation, which identifies tissue regions, or registration, which aligns images or structures. Detecting these problems before interpretation helps prevent unreliable measurements from being treated as real biological change and supports greater confidence in imaging-based medical decisions.
A practical assessment begins with image frames collected over time, followed by analysis of selected landmarks, intensity patterns, contours, or motion. The resulting correspondence is examined for consistency while considering deformation, noise, and changes in image quality. Investigators can then identify tracking, segmentation, or registration errors and judge whether the measurements are sufficiently reliable for the intended analysis.
It is particularly relevant when clinicians or researchers need to quantify changing anatomy rather than inspect a single image. Examples include cardiac motion analysis, tumor monitoring, image-guided procedures, treatment planning, and longitudinal disease assessment. In these settings, reliable tracking supports interpretation of tissue movement or change and can strengthen confidence in measurements used to inform medical decisions.
Consistent tracking allows tissue position, shape, or motion to be compared across image frames and over longer periods of assessment. This supports quantitative evaluation of change during disease monitoring and provides information that can contribute to treatment planning. The value depends on recognizing tracking errors, because inaccurate correspondence could make disease progression or treatment-related change appear different from its actual course.