Computer Vision News - June 2020

3 Summary AI in Cardiac MRI Segment tion 21 AI in Cardiac MRI – Methods: 1. Segmentation: Responding to the first ACDC challenge, over the past few years there has been significant advancement in developing deep learning networks for the automatic segmentation of the LV, LV myocardium, and RV. Generally, the approach applies CNNs based on U-Net and/or ResNet that are trained on the ACDC dataset. Regression-based methods using novel multiview CNNs utilize long- and short- axis images as input to directly predict left ventricular ejection fraction (LVEF) and left ventricular volumes at ED and ED. Multiscale residual DenseNets have likewise been successfully applied to cardiacsegmentation.Otherapproaches combine deformable models and deep learning method for LV segmentation. 2. Classification: The successful application of automatic deep learning segmentation from cardiac magnetic resonance facilitates the classification and diagnosis of cardiac pathologies. The current ACDC challenge classification front runner utilized a parameter and memory- efficient FCN architecture based on DenseNets in combination with an up- sampling path and dual loss function to process the input images at multiple scales and viewpoints simultaneously. Another approach applies a fully automatic neural architecture to accurately and reliably identify the presence, position, transmurality, and size of chronic MI. Results: State-of-the-art deep learning segmentation and classificationmethods for CMR are on par with human expert performance. CNN-based segmentation seems to be even faster and more accurate than manual delineation and also outperforms other state-of-the- art medical imaging technologies while deriving clinically relevant measures necessary for diagnosis. An additional outcome of automation apart from speed, precision and ease is that these techniques may be optimized to detect and delineate CAD without the need for a gadolinium-based contrast agent. Conclusion: Deep learning is being increasingly and successfully applied to facilitate fully- automatic and accurate segmentation of cardiac structures and classifying pathologies in cardiac magnetic resonance, thereby significantly reducing the postprocessing requirements of cardiac function analysis. This is accelerating patient care workflow, improving accuracy, and reducing clinician workload. RSIP Vision is the leading expert in this kind of work. Read about our projects in AI for cardiology . Take us along for your next deep learning projects !