Endocardial Border Detection
Endocardial Border Detection - Related Videos
09:17
High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
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Cited by 20 •
2011
This report provides a detailed description of the methodology and results of simultaneous endocardial and epicardial optical mapping of electrical excitation in the intact left atrium of a Langendorff-perfused sheep heart during stretch-induced atrial fibrillation.
09:38
Following Endocardial Tissue Movements via Cell Photoconversion in the Zebrafish Embryo
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Cited by 6 •
2018
This protocol describes a method for the photoconversion of Kaede fluorescent protein in endocardial cells of the living zebrafish embryo that enables the tracking of endocardial cells during atrioventricular canal and atrioventricular heart valve development.
08:57
En Face Endocardial Cushion Preparation for Planar Morphogenesis Analysis in Mouse Embryos
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Cited by 2 •
2022
Classically, the endocardium of the mouse embryonic valve primordium has been analyzed using transversal, coronal, or sagittal sections. Our novel approach for en face, two-dimensional imaging of the endocardium at valvulogenic regions allows planar polarity and cell rearrangement analysis of the endocardium during valve development.
08:22
Isolation of Endocardial and Coronary Endothelial Cells from the Ventricular Free Wall of the Rat Heart
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Cited by 13 •
2020
We present a protocol for the isolation of endocardial and coronary endothelial cells from rat hearts through sequential tissue digestion in a digestion buffer, cell collection from recurrent centrifuge cycles, and cell purification using anti-rat CD31 microbeads.
09:10
Combination of Adhesive-tape-based Sampling and Fluorescence in situ Hybridization for Rapid Detection of Salmonella on Fresh Produce
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Cited by 17 •
2010
This protocol describes a simple adhesive-tape-based approach for sampling of tomato and other fresh produce surfaces, followed by rapid whole cell detection of Salmonella using fluorescence in situ hybridization (FISH).
FAQs
The analysis combines changes in image intensity, texture, motion, and anatomical continuity to follow the ventricular contour from one phase of the cardiac cycle to the next. Using several features helps maintain a consistent boundary as the heart moves, supporting measurements that reflect changing ventricular geometry rather than relying on a single visual signal.
Anatomical continuity helps the detected contour remain connected and consistent across neighboring image regions and successive cardiac phases. This constraint can support a more coherent representation of ventricular shape, particularly when local image appearance varies. A continuous contour is important because subsequent volume and wall-motion assessments depend on the integrity of the traced boundary.
User guidance can work alongside automated segmentation by helping direct or refine the contour when image features do not uniquely identify the desired boundary. The automated process then applies image-based information across the cardiac cycle, while user input provides interpretive control. This combined approach may reduce variability compared with relying entirely on unstructured visual interpretation.
Reliability depends on how clearly the ventricular boundary can be distinguished through intensity, texture, motion, and anatomical continuity in echocardiographic images. When these cues provide a consistent pattern, the contour can be followed more effectively through the cardiac cycle. The quality of this contour directly affects the accuracy and consistency of derived cardiac measurements.
A typical workflow begins with echocardiographic image acquisition, followed by identification of the ventricular boundary using image-based features. The contour is then traced across the cardiac cycle, either automatically or with user guidance. Once the sequence of contours is available, it supports calculation or assessment of ventricular volume, ejection fraction, and wall motion.
The contours provide a structured basis for evaluating ventricular volume, ejection fraction, and wall motion. These measurements help characterize heart structure and function beyond qualitative image review. In medicine, the resulting quantitative information can contribute to cardiac diagnosis and treatment planning, while also supporting more consistent interpretation across examinations or observers.
Endocardial border detection enables cardiac images to be converted into quantitative contour-based information, making ventricular function easier to analyze systematically. Its ability to support measurements across the cardiac cycle is relevant to research on automated cardiac-function analysis. The method can also reduce interpretation variability, helping investigators study reproducible measures of structure and performance.