M11-093L: Development of a Highly Efficient and User-Friendly Software System for Carotid Intima-Media Thickness- Researchers at Arizona State University have developed a highly user-friendly system for semiautomatic CIMT image interpretation. Their contribution is the application of active contour models (snake models) with hard constraints, leading...

Deep convolutional neural networks (CNN) are useful in a variety of applications ranging from computer vision to signal processing. There is increasing interest in applying CNN to biomedical image analysis, but success is impeded by the lack of large annotated datasets in biomedical imaging. Expert annotation is tedious, time consuming and expensive...

Pulmonary embolism (PE) is a common cardiovascular emergency. Mortality rates approaching 30% are common with undiagnosed PE, but they fall to 2% with early diagnosis and treatment, thus, quick and accurate diagnosis is critical. Suspected PE is typically diagnosed with CT pulmonary angiography (CTPA), but this technique has issues with interpretation...

Deep convolutional neural networks (CNN) are tremendously successful in a variety of applications ranging from computer vision to signal processing. There is increasing interest in applying CNN to biomedical image analysis, but success is impeded by the lack of large annotated datasets in biomedical imaging. Expert annotation is tedious, time consuming...

Carotid Intima-Media Thickness (CIMT) measurement is a non-invasive, sensitive, and highly reproducible technique for cardiovascular risk stratification. Interpreting a CIMT video involves three operations: (1) select three end-diastolic ultrasound frames (EUFs) in each video; (2) localize a region of interest (ROI) approximately 1 cm distal from the...