Fully Automatic Cardiac Segmentation And Quantification For Pulmonary Hypertension Analysis Using Mice Cine Mr Images

Abstract

Pulmonary Hypertension (PH) induces anatomical changes in the cardiac muscle that can be quantitativly assessed using Magnetic Resonance (MR). Yet, the extraction of biomarkers relies on the segmentation of the affected structures, which in many cases is performed manually by physicians. Previous approaches have shown successful automatic segmentation results for different heart structures from human cardiac MR images. Nevertheless, the segmentation from mice images is rarely addressed, but it is essential for preclinical studies. Thus, the aim of this work is to develop an automatic tool based on a convolutional neural network for the segmentation of 4 cardiac structures at once in healthy and pathological mice to precisely evaluate biomarkers that may correlate to PH. The obtained automatic segmentations are comparable to manual segmentations, and they improve the distinction between control and pathological cases, especially regarding biomarkers from the right ventricle.