Image Analysis Platform
Image Analysis Platform - Related Videos
08:34
Field-Deployable Lens-Free Imaging Platform for Rapid Label-Free Analysis of Natural Killer Cell Activation
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Cited by 1 •
2025
This protocol describes a label-free method for the rapid single-cell analysis of natural killer (NK) cell count and activity using lens-free shadow imaging technology. This method quantifies morphological changes in individual NK cells by computerized analysis of their diffraction patterns.
04:47
Multimodal Optical Imaging Platform for Studying Cellular Metabolism
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2025
Utilizing a multimodal platform combining label-free optical imaging modalities, we have developed a protocol for visualizing and quantifying cellular dynamics and metabolism. Through imaging via multiphoton fluorescence, second harmonic generation, and stimulated Raman scattering microscopy, we can generate a holistic overview of the cellular and molecular environment.
07:46
Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis
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2023
Here, a digital immunohistochemistry image analysis platform was developed and validated to quantitatively analyze the endometrial immune cells of patients with recurrent miscarriages in the window of implantation.
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
10:35
Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis
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2016
The assembly and use of a multimodal microendoscope is described which can co-register superficial tissue image data with tissue physiological parameters including hemoglobin concentration, melanin concentration, and oxygen saturation. This technique can be useful for evaluating tissue structure and perfusion, and can be optimized for individual needs of the investigator.
FAQs
Noise reduction suppresses unwanted variation, while contrast enhancement makes relevant structures easier to distinguish from their background. These steps prepare microscopy images for segmentation, the separation of cells, tissues, organelles, or other structures from surrounding regions. Consistent preprocessing is important because the quality of this separation directly affects later measurements of number, size, shape, and intensity.
Segmentation identifies which image regions correspond to the biological structures being studied. Once cells, tissues, or organelles are separated from the background, the platform can calculate features for those regions rather than treating the entire image as one signal. This makes quantitative comparisons possible across microscopy images and supports analyses focused on specific structures or populations.
Feature extraction converts identified image regions into measurable characteristics. Common outputs supported by the platform include object size, shape, intensity, number, and spatial distribution. Together, these measurements can describe differences in cell or tissue appearance, abundance, and arrangement, allowing visual observations to become quantitative evidence for biological comparisons.
An Image Analysis Platform can reduce reliance on manual scoring by applying consistent processing and measurement steps across many images. This supports improved reproducibility and makes large imaging datasets more practical to analyze. Manual review may still inform interpretation, but standardized quantitative outputs allow researchers to compare samples using defined properties rather than visual judgment alone.
A practical workflow begins with digital microscopy images, followed by noise reduction and contrast enhancement when needed. The user then segments structures of interest from the background and applies feature extraction to measure properties such as size, shape, intensity, number, or spatial distribution. The resulting measurements provide a quantitative basis for comparing biological samples or conditions.
Biologists may use this type of platform for cell phenotyping, developmental studies, disease research, and drug evaluation. In these settings, image-derived measurements can characterize cellular or tissue features, track differences among samples, and support comparisons across experiments. The approach is especially useful when studies generate many microscopy images or require reproducible quantitative assessment.