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Changes in mechanical properties of myocardium caused by a infarction can lead to kinematic abnormalities. This phenomenon has inspired us to develop this work for delineation of myocardial infarction area directly from non-contrast agents cardiac MR imaging sequences. The main contribution of this work is to develop a new joint motion feature learning architecture to efficiently establish direct correspondences between motion features and tissue properties. This architecture consists of three seamless connected function layers: the heart localization layers can automatically crop the region of interest (ROI) sequences involving the left ventricle from the cardiac MR imaging sequences; the motion feature extraction layers, using long short-term memory-recurrent neural networks, a) builds patch-based motion features through local intensity changes between fixed-size patch sequences (cropped from image sequences), and b) uses optical flow techniques to build image-based features through global intensity changes between adjacent images to describe the motion of each pixel; the fully connected discriminative layers can combine two types of motion features together in each pixel and then build the correspondences between motion features and tissue identities (that is, infarct or not) in each pixel. We validated the performance of our framework in 165 cine cardiac MR imaging datasets by comparing to the ground truths manually segmented from delayed Gadolinium-enhanced MR cardiac images by two radiologists with more than 10 years of experience. Our experimental results show that our proposed method has a high and stable accuracy (pixel-level: 95.03%) and consistency (Kappa statistic: 0.91; Dice: 89.87%; RMSE: 0.72 mm; Hausdorff distance: 5.91 mm) compared to manual delineation results. Overall, the advantage of our framework is that it can determine the tissue identity in each pixel from its motion pattern captured by normal cine cardiac MR images, which makes it an attractive tool for the clinical diagnosis of infarction.