Glossary Term
Image Segmentation
Definition
Image segmentation is a process in computer vision and image analysis that divides a digital image into multiple regions or segments to simplify its representation and make it easier to analyze. Each segment groups pixels that share certain characteristics, such as color, intensity, or texture. In medical imaging, segmentation is commonly used to isolate anatomical structures, identify abnormalities, or assist in diagnosis.
Relevance to the MedTech Industry
Image segmentation enables precise analysis of medical images (e.g., MRIs, CT scans, and X-rays) by extracting meaningful regions, such as organs, tissues, or tumors. This improves the accuracy of diagnoses, treatment planning, and monitoring of disease progression.
Additional Information & Related Terms
Types of Image Segmentation in Medical Imaging
Threshold-Based Segmentation
Separates regions by pixel intensity thresholds.
Example: Identifying bones in an X-ray by their high intensity.
Region-Based Segmentation
Groups pixels based on similarity in a specific area.
Example: Segmenting tumors by detecting uniform tissue density in CT scans.
Edge-Based Segmentation
Detects object boundaries by identifying rapid changes in pixel intensity.
Example: Highlighting organ outlines in MRI images.
Clustering-Based Segmentation
Groups pixels into clusters using algorithms like k-means.
Example: Differentiating healthy and diseased tissues in a brain MRI.
Deep Learning-Based Segmentation
Utilizes neural networks to learn complex patterns in medical images.
Example: Segmenting lung nodules in CT scans with convolutional neural networks (CNNs).
Examples of Applications in Medicine
Tumor Detection: Isolating and quantifying tumor regions in CT or MRI scans.
Organ Segmentation: Segmenting liver, lungs, or kidneys for volumetric analysis.
Blood Vessel Analysis: Identifying vascular structures in angiograms or CT scans.
Cardiac Imaging: Segmenting heart chambers to assess function or structural abnormalities.
Radiotherapy Planning: Delineating target regions and surrounding tissues to optimize radiation doses.
Related Terms
Computer Vision: A field of AI focused on extracting meaningful information from images and videos.
Convolutional Neural Networks (CNNs): Deep learning models commonly used for image segmentation tasks.
Semantic Segmentation: Assigning a class label to each pixel in an image (e.g., "tumor" or "background").
Instance Segmentation: Differentiating between individual objects within the same class (e.g., multiple tumors).
Radiomics: Extracting quantitative features from medical images to aid in diagnosis or treatment.