Mri Tissue Segmentation
Mri Tissue Segmentation - Related Videos
04:25
Manual Segmentation of the Human Choroid Plexus Using Brain MRI
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Cited by 13 •
2023
Despite the crucial role of the choroid plexus in the brain, neuroimaging studies of this structure are scarce due to the lack of reliable automated segmentation tools. The present protocol aims to ensure gold-standard manual segmentation of the choroid plexus that can inform future neuroimaging studies.
04:37
Sampling Prostate Cancer Tissue for Biobanking: An MRI-guided Targeted Technique to Remove Biopsy Punches from Fresh Tumor Tissue
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2023
This video demonstrates an MRI data-based technique to target specific tumor-containing regions in prostate tissue for sampling. The collected biopsy specimens can be either stored for biobanking purposes or used for subsequent downstream analysis.
10:25
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Cited by 30 •
2019
Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.
06:48
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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2019
This protocol describes the process of applying seven different automated segmentation tools to structural T1-weighted MRI scans to delineate grey matter regions that can be used for the quantification of grey matter volume.
04:11
DCE-MRI of Orthotopic Pancreatic Tumor Xenografts: A Method to Assess Microvasculature in a Target Tumor Tissue
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2023
This video describes the technique of DCE-MRI for orthotropic pancreatic tumor xenografts in mice. DCE-MRI is a non-invasive method to analyze microvasculature in a target tissue, and useful to assess vascular response in a tumor following a novel therapy. This method will assist investigators to apply DCE-MRI for orthotopic gastrointestinal cancer mouse models.
FAQs
Voxel classification depends on several image characteristics rather than intensity alone. Contrast helps distinguish tissues with different signal appearances, while texture and spatial context provide additional information about local patterns and anatomical relationships. Combining these cues helps separate regions that may appear similar on a single measure and supports more meaningful anatomical or biological labeling.
Manual delineation assigns tissue boundaries through human interpretation, whereas atlas-based and automated machine-learning approaches provide more computationally assisted labeling. These strategies represent different ways to convert MRI information into tissue classes. Their selection depends on the analysis needs, desired workflow, and importance of obtaining measurable structures consistently across images.
Image noise, patient motion, and differences among MRI protocols can all affect segmentation accuracy. These factors may alter intensity, contrast, texture, or spatial appearance, making tissue boundaries less distinct or less consistent. As a result, measurements derived from the segmented regions may become less reliable, particularly when images are compared across examinations or study sites.
A typical workflow begins with an MRI image and an objective, such as examining brain tissues, a lesion, or a tumor. The analyst or algorithm then uses image characteristics and spatial information to assign voxels to relevant classes. The resulting regions can be reviewed or analyzed as measurable anatomical or biological structures.
MRI tissue segmentation supports several medical applications, including brain morphometry, lesion quantification, treatment planning, and longitudinal monitoring. By converting image regions into measurable structures, it allows researchers and clinicians to assess tissue characteristics and lesion extent more systematically. These measurements can support diagnostic assessment and help evaluate changes over time.
Reliable segmentation improves the reproducibility of measurements made from MRI images, which is especially important when tracking anatomical or biological changes over multiple examinations. Consistent tissue labeling helps distinguish actual changes from variation caused by noise, motion, or differing MRI protocols. This makes longitudinal monitoring and clinical research comparisons more dependable.