Merge OEM Image Segmentation | Download Product Sheet

Normally obscured by dense tissue, femoral arteries and an associated stent become visible through Semi-Automatic Segmentation.

Advanced Image Analysis Through Automated Anatomical Segmentation Healthcare professionals oftentimes need to uniquely identify certain anatomical structures in order to view and measure underlying anatomy. When working with digital images, selection and segmentation is normally performed using drawing tools to manually trace the contours of a specific region of interest, which can then be hidden or displayed depending on a user's preference. Manual segmentation presents a number of workflow roadblocks: to begin with, tracing the individual contours of an anatomical region can be a time consuming process; largely dependent on subjective judgment, manual segmentation can produce different results from different physicians (consequently leading to different diagnoses).

Revolutionizing the way that clinical specialists manipulate medical images, Merge OEM's image segmentation technologies provide fast, efficient and consistent means for separating anatomical structures.

Consistent & Efficient Results for Segmentation Working with images from all modalities, in both 2D and 3D formats, Merge OEM’s Segmentation technologies include:

Image Segmentation plays an important role in identifying key characteristics of obscured anatomy.

Automatic Segmentation
Using ImageSnap Technology™, which automatically detects and measures a variety of anatomy, automatic segmentation provides immediate separation of key structures. Users have the opportunity to refine, accept or reject computer-generated findings.

Semi Automatic Segmentation
Incorporating the latest in Point and Click Classification Technology™ (PCC), Semi Automatic Segmentation requires users to select a region of interest, and scale the selection for specificity.

A Single Solution for All Modalities Automated Segmentation Technologies can be applied to a variety of different clinical specialties. When incorporated into an imaging application, it can be used to segment images (and generate data) from all major modalities in both 2D and 3D formats. In the case of cardiology, for example, automatic segmentation divides the heart into individual chambers and valves, generating a wide range of clinically relevant metrics.

For cardiology, transmurality and wall thickness of the left ventricle are easily viewed using Merge OEM's Automated Segmentation Technologies.

Fast and Efficient Workflow for Cardiology Cardiology provides an example for the utility of automatic segmentation. During preliminary investigation, the effectiveness of a patient's heart is often examined through an echocardiogram, angiogram, MRI or CT imaging. In examining these images, cardiologists derive a variety of different metrics that help to determine the effectiveness of the heart, nature of the problem and consequent diagnosis. Common metrics include ejection fraction, regional wall thickness, regional wall motion, surface area, volume, regurgitation, stenoses and aneurisms*. Using traditional techniques, an image must be manually segmented in order determine these metrics. With Merge OEM's Segmentation Technologies, workstations can be configured to automatically generate these figures during real-time image acquisition or loading. This process can save an immeasurable quantity of a specialist’s time which would normally be spent performing manual calculations. Merge OEM's Automatic Segmentation Technology generates a bullseye plot which divides the heart into 16 separated sections, in turn identifying key structures such as the right/left ventricle, right/left atrium, mitral valve and aorta.

Dynamic images such as echocardiograms can be easily analyzed in real-time with the assistance of Automatic Segmentation and tracking.

Image Analysis for a variety of different contexts
At the root of Merge OEM's approach to automatic segmentation are a variety of image analysis algorithms known as ImageSnap Technology. When applied in different contexts, ImageSnap can be used to automate image analysis in different contexts including:

• Automatic Bone Removal
• Liver Analysis
• Prostate Analysis
• Orthopaedics
• Biometric Analysis
• Baggage Screening