We propose a method to classify cardiac pathology based on a novel approach
to extract image derived features to characterize the shape and motion of the
heart. An original semi-supervised learning procedure, which makes efficient
use of a large amount of non-segmented images and a small amount of images
segmented manually by experts, is developed to generate pixel-wise apparent
flow between two time points of a 2D+t cine MRI image sequence. Combining the
apparent flow maps and cardiac segmentation masks, we obtain a local apparent
flow corresponding to the 2D motion of myocardium and ventricular cavities.
This leads to the generation of time series of the radius and thickness of
myocardial segments to represent cardiac motion. These time series of motion
features are reliable and explainable characteristics of pathological cardiac
motion. Furthermore, they are combined with shape-related features to classify
cardiac pathologies. Using only nine feature values as input, we propose an
explainable, simple and flexible model for pathology classification. On ACDC
training set and testing set, the model achieves 95% and 94% respectively as
classification accuracy. Its performance is hence comparable to that of the
state-of-the-art. Comparison with various other models is performed to outline
some advantages of our model.

提出一种基于图像特征的心脏病理分类方法，其中使用半监督学习程序来提取心脏运动的形状和运动特征，生成心肌和心腔的时间序列运动特征，并结合形状特征进行分类，取得了高检测准确率。