Itk-snap Segmentation
Itk-snap Segmentation - Related Videos
08:04
Evaluating the Accuracy of Snap Judgments
0 Views •
2023
Source: Diego Reinero & Jay Van Bavel—New York University Social psychologists have long been interested in the way people form impressions of others. Much of this work has focused on the errors people make in judging others, such as the exaggerated influence of central traits (such as "warm" and "cold"), the insufficient weight given to the context in which others' behavior takes place, and the tendency for people to make judgments that conform to their initial expectations about another.
04:36
A Snap Chip Technology for a Cross-Reactivity-Free Multiplexed Sandwich Immunoassay
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2025
The video showcases the implementation of snap chip technology for cross-reactivity-free multiplexed sandwich immunoassays. The simple action of snapping two microarray slides, each containing different reagents, ensures reagent transfer without cross-contamination and helps to detect multiple proteins using sandwich immunoassay.
10:44
Snap Chip for Cross-reactivity-free and Spotter-free Multiplexed Sandwich Immunoassays
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Cited by 3 •
2017
We demonstrate a snap chip technology for performing cross-reactivity-free multiplexed sandwich immunoassays by simply snapping two slides. A snap apparatus is used for reliably transferring reagents from microarray-to-microarray. The snap chip can be used for any biochemical reactions requiring colocalization of different reagents without cross-contamination.
07:38
Fluorescent Labeling of COS-7 Expressing SNAP-tag Fusion Proteins for Live Cell Imaging
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Cited by 8 •
2010
SNAP-tag and CLIP-tag protein labeling systems enable the specific, covalent attachment of molecules, including fluorescent dyes, to a protein of interest in live cells. Once cloned and expressed, the tagged protein can be used with a variety of substrates for numerous downstream applications without having to clone again.
03:24
Anterior Eye Segment Dissection: A Method to Isolate Cornea Bearing Anterior Segment from Porcine Eye
0 Views •
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
Active-contour tools evolve a boundary toward image features, especially intensity differences. Rather than requiring every edge to be outlined manually, the method uses those image characteristics to help place a boundary around a target structure or lesion. Users can inspect the evolving result across linked image views, connecting algorithmic guidance with anatomical interpretation.
Manual outlining lets the user directly delineate an anatomical structure or lesion, whereas semi-automatic segmentation combines user interaction with image-based boundary evolution. ITK-SNAP supports both approaches, allowing the delineation strategy to incorporate direct anatomical judgment and intensity-driven assistance. This combination is useful when image features can guide part of the boundary but still require visual inspection.
Linked axial, sagittal, coronal, and 3D views provide complementary perspectives on the same delineated structure. A boundary that appears appropriate in one plane can be examined in the others, while the 3D view shows its overall spatial form. Together, these views help users inspect anatomical extent before using the resulting segmentation for measurements or visualization.
A typical workflow examines the MRI or related imaging data, identifies the brain structure or lesion of interest, and delineates it with manual outlining, semi-automatic tools, or both. Active-contour methods can help move boundaries toward image features, after which users inspect the result through linked 2D and 3D views and produce a label map.
Label maps preserve the delineated regions as identifiable segmented structures within the imaging data. They can support volume measurements, visualization, and comparisons between groups, making anatomical or disease-related differences more amenable to quantitative analysis. In neuroscience, this connects image-based delineation with studies of brain anatomy and disease rather than limiting the result to visual inspection.
Neuroscientists can apply the approach when MRI or related imaging data contain brain regions, tumors, vascular abnormalities, or other structures that require delineation. The resulting segmentations may support anatomical visualization, volume measurement, group comparisons, quantitative investigations of disease, and surgical planning. Its relevance therefore spans both basic studies of brain anatomy and clinically oriented imaging analysis.