Image-derived Approach
Image-derived Approach - Related Videos
10:53
Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
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Cited by 10 •
2017
This protocol outlines the implementation of image-guided, laser-based hydrogel degradation to fabricate vascular-derived, biomimetic microfluidic networks embedded in poly(ethylene glycol) diacrylate (PEGDA) hydrogels. These biomimetic microfluidic systems may be useful for tissue engineering applications, generation of in vitro disease models, and fabrication of advanced "on-a-chip" devices.
10:44
A Guided Materials Screening Approach for Developing Quantitative Sol-gel Derived Protein Microarrays
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Cited by 1 •
2013
A guided material screening approach to develop sol-gel derived protein doped microarrays using an emerging pin-printing method of fabrication is described. This methodology is demonstrated through the development of acetylcholinesterase and multikinase microarrays, which are used for cost-effective small-molecule screening.
08:24
Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
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2016
This protocol describes a method to quantify mouse sociability. Mice are videotaped as they move and interact in a special cage. Movie processing allows for the automated quantification of sociability with excellent accuracy and reliability.
09:43
In vivo Near Infrared Fluorescence (NIRF) Intravascular Molecular Imaging of Inflammatory Plaque, a Multimodal Approach to Imaging of Atherosclerosis
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Cited by 32 •
2011
We detail a new near-infrared fluorescence (NIRF) catheter for 2-dimensional intravascular molecular imaging of plaque biology in vivo. The NIRF catheter can visualize key biological processes such as inflammation by reporting on the presence of plaque-avid activatable and targeted NIR fluorochromes. The catheter utilizes clinical engineering and power requirements and is targeted for application in human coronary arteries. The following research study describes a multimodal imaging strategy...
13:13
Generation of Bone Marrow Derived Murine Dendritic Cells for Use in 2-photon Imaging
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Cited by 72 •
2008
Antigen presentation in secondary lymphoid organs by dendritic cells is crucial for the initiation of the T cell mediated adaptive immune response. Here we demonstrate the culture of bone marrow derived murine dendritic cells, activation, and labeling for 2-photon imaging.
FAQs
Preprocessing prepares acquired images for analysis, while segmentation identifies the anatomical or functional regions from which measurements can be made. These steps determine what information enters later feature extraction and modeling, so inconsistent handling can affect comparability. In medical research, careful attention to them supports more reproducible measurements across patients, studies, and time points.
Feature extraction converts selected image information into measurable characteristics, allowing visual patterns to become structured variables rather than remaining descriptive impressions. Those variables can then undergo statistical or machine-learning analysis to identify relationships or generate clinically relevant results. This makes image findings easier to compare with clinical information and outcomes.
Statistical and machine-learning analyses can identify patterns in image-derived variables, but the resulting measurements do not stand alone. Interpreting them alongside clinical information helps connect quantitative image findings with diagnosis, treatment response, disease mechanisms, or clinical outcomes. This context is essential when translating measurements into medically meaningful conclusions rather than treating them as isolated numerical results.
An imaging biomarker emerges when image-derived measurements provide a structured signal that can be connected with a clinical question or outcome. The approach helps move from a visual observation to a potentially trackable indicator of anatomical or functional change, or treatment response. In research, this supports comparisons between patient-specific imaging findings and clinical results.
Medical investigators can apply these methods to diagnosis, treatment planning, and response monitoring. Quantitative image information may add detail to clinical assessment by describing anatomical or functional changes in a reproducible form. The same strategy also supports research studies that compare imaging findings with disease mechanisms or clinical outcomes, rather than limiting use to a single clinical decision.
Longitudinal use is relevant when the goal is to assess change across time points. Applying comparable image analysis to repeated observations can produce measurements that help characterize change or response, while interpretation remains linked to clinical information. This creates a structured way to examine whether patient-specific imaging changes correspond with treatment-related or disease-related outcomes.