2D left ventricle segmentation using deep learning

Abstract

Automatic segmentation of the left ventricle (LV) can become a useful tool in echocardiography, for instance to provide automatic ejection fraction measurements or to initialize deformation imaging algorithms. Deep neural networks have recently shown very promising results for improving image classification and segmentation. These methods learn using only a set of input and output data, but require a large and representative amount of annotated data to be successful. This means an expert has to draw the LV border in potentially thousands of images, which is highly tedious and time consuming.