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Medical image analysis, especially segmenting a specific organ, has animportant role in developing clinical decision support systems. In cardiacmagnetic resonance (MR) imaging, segmenting the left and right ventricles helpsphysicians diagnose different heart abnormalities. There are challenges forthis task, including the intensity and shape similarity between left ventricleand other organs, inaccurate boundaries and presence of noise in most of theimages. In this paper we propose an automated method for segmenting the leftventricle in cardiac MR images. We first automatically extract the region ofinterest, and then employ it as an input of a fully convolutional network. Wetrain the network accurately despite the small number of left ventricle pixelsin comparison with the whole image. Thresholding on the output map of the fullyconvolutional network and selection of regions based on their roundness areperformed in our proposed post-processing phase. The Dice score of our methodreaches 87.24% by applying this algorithm on the York dataset of heart images.

Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network