ScanEV

ScanEV performs automated detection and morphometric analysis of extracellular vesicles (EVs), including exosomes, in transmission electron microscopy (TEM) images to quantify size and shape characteristics.

Key Features:

• Automated Detection: Identifies "cup-shaped" particles characteristic of extracellular vesicles such as exosomes within TEM images.
• Morphometric Analysis: Calculates size and shape metrics of detected EVs to provide quantitative morphometric data.
• Convolutional Neural Network (CNN): Uses a CNN-based deep learning architecture for image recognition to detect and characterize EVs in TEM images.

Scientific Applications:

• EV Characterization: Provides precise morphometric data for characterization of extracellular vesicles and exosomes.
• Cell Communication Studies: Supports investigations into EV-mediated cell communication by quantifying vesicle morphology.
• Disease Mechanisms and Therapeutic Research: Aids studies of disease mechanisms and potential therapeutic applications by supplying quantitative EV morphometrics in physiological and pathological contexts.

Methodology:

ScanEV applies a convolutional neural network trained on TEM image datasets to recognize EV features and compute morphometric measurements, with model performance improving as it learns from larger datasets.