X-Ray Anomaly Segmentation and Defect Detection
Deep learning-based tools help detect and segment anomalies in X-ray images
Graphical programming environment for deep learning-based industrial image analysis
The search for biological anomalies in radiological X-rays, ultrasounds, and NMRs has traditionally required the flexibility of a human inspector. Today, deep learning-based defect detection and segmentation tools can help identify anomalies in medical images quickly and accurately. Whether searching for a specific anomaly or any deviation from the body’s normal appearance, Cognex Deep Learning combines the flexibility of a human inspector with the speed and robustness of a computerized system.
The defect detection tool can be used to inspect a medical X-ray image or detect defects on an ultrasonic image simply by learning the normal appearance of an object, including its significant but tolerable variations. The defect detection tool develops a reference model of an organ’s normal appearance, as well as specific types of anomalies, based on training on a set of sample images. Any anomalies which digress from the normal physiology of the targeted zone are flagged as defects for a CAD computer-aided diagnosis by an expert radiologist.