A combined deep-learning approach to fully automatic left ventricle segmentation in cardiac magnetic resonance imaging

摘要

In clinical practice, cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for theevaluation of function and structure of the left ventricle (LV). However, the quantification of LV parameters in allframes, even when performed by experienced radiologists, is very time consuming mainly due to the inhomogeneity ofcardiac structures within each image, the variability of the cardiac structures across subjects and the complicatedglobal/regional temporal deformation of the myocardium during the cardiac cycle. In this work, we employed acombination of two convolutional neural networks (CNN) to develop a fully automatic LV segmentation method forShort Axis CMR datasets. The first CNN defines the region of interest (ROI) of the cardiac chambers based on You OnlyLook Once (YOLO) network. The output of YOLO net is used to filter the image and feed the second CNN, based on UNetnetwork, which segments the myocardium and the blood pool. The method was validated in CMR exams of 59individuals from an institutional clinical protocol. Segmentation results, evaluated by metrics Percentage of GoodContours, Dice Index and Average Perpendicular distance, were 98,59% ± 4,28%, 0,93 ± 0,06 and 0,72 mm ± 0,62mm, respectively, for the LV epicardium, and 94,98% ± 14,04%, 0,86 ± 0,13 and 1,19 mm ± 1,29 mm, respectively,for the LV endocardium. The combination of two CNNs demonstrated good performance in terms of the evaluatedmetrics when compared to literature results.