Computer Vision News - February 2020

3 Summary Deep Learning for Echocardiogr phy 19 often require intervention in the form of initial training - manual ly tracing cardiac structures on dataset images - and correction. Therefore, developing AI -based, ful ly automated solutions, wi l l substantial ly increase the accuracy and robustness of the LV evaluation and diagnosis whi le saving time and money. Methods: Convolutional Neural Networks, for example, U-net, are trained for shape constraint and uti l ized to automate LV segmentation quickly and more accurately. CNN’s are trained to locate the ROI , in this case, the LV, and these local izations may be used as input for ref ined segmentation and classi f ication, for example by applying sparse mani fold learning or deformable models. Other approaches are working towards a ful ly automated method that wi l l reduce computational burden and time- intensive training. Results : Two-step methods have shown eff icacy in segmentation. For example, in a series of studies, Carneiro et al . used unsupervised DBNs to predict landmarks for def ining the segmentation contour, then these ROIs were used as input for local ization. Dong et al . and Ghesu et al . also combined deep learning with traditional methods, speci f ical ly deformable shape models, to segment the left ventricle, resulting in a demonstrated improvement in segmentation qual ity of at least 40% over the previous state-of- the-art method. 2.Technical Challenge: View Classification Echocardiography rel ies on expert interpretation of multiple images from a range of non-consecutive views - a process that is both time- consuming and prone to human error. One critical task toward computer-assisted medicine is determining that computers can recognize and classi fy various images and views. Methods: CNN architectures for image recognition, for example, VGGNet, are trained on a chal lenge dataset of manual ly label led images to learn how to classi fy echocardiographic images from multiple non- sequential views. Unsupervised deep models that do not require the time- intensive process of manual label l ing are also being tested, for example by applying an auto-encoder.