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Regional heart motion abnormality detection: An information theoretic approach

机译：区域性心脏运动异常检测：一种信息理论方法

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摘要

Tracking regional heart motion and detecting the corresponding abnormalities play an essential role in the diagnosis of cardiovascular diseases. Based on functional images, which are subject to noise and segmentation/registration inaccuracies, regional heart motion analysis is acknowledged as a difficult problem and, therefore, incorporation of prior knowledge is desirable to enhance accuracy. Given noisy data and a nonlinear dynamic model to describe myocardial motion, an unscented Kalman smoother is proposed in this study to estimate the myocardial points. Due to the similarity between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem. We use the Shannon's differential entropy of the distributions of potential classifier features to detect and locate regional heart motion abnormality. A naive Bayes classifier algorithm is constructed from the Shannon's differential entropy of different features to automatically detect abnormal functional regions of the myocardium. Using 174 segmented short-axis magnetic resonance cines obtained from 58 subjects (21 normal and 37 abnormal), the proposed method is quantitatively evaluated by comparison with ground truth classifications by radiologists over 928 myocardial segments. The proposed method performed significantly better than other recent methods, and yielded an accuracy of 86.5% (base), 89.4% (mid-cavity) and 84.5% (apex). The overall classification accuracy was 87.1%. Furthermore, standard kappa statistic comparisons between the proposed method and visual wall motion scoring by radiologists showed that the proposed algorithm can yield a kappa measure of 0.73.
机译：跟踪局部心脏运动并检测相应的异常在心血管疾病的诊断中起着至关重要的作用。基于易受噪声和分割/配准误差影响的功能图像，区域心脏运动分析被认为是一个难题，因此，需要结合现有技术以提高准确性。考虑到嘈杂的数据和描述心肌运动的非线性动力学模型，本研究提出了一种无味的卡尔曼平滑器来估计心肌点。由于正常和异常心脏运动的统计信息之间的相似性，检测和分类异常是一个具有挑战性的问题。我们使用潜在分类器特征分布的香农微分熵来检测和定位区域性心脏运动异常。一个朴素的贝叶斯分类器算法由香农的不同特征的微分熵构造而成，以自动检测心肌的异常功能区域。使用从58位受试者（21位正常和37位异常）中获得的174个分段短轴磁共振电影，通过与928个心肌节段的放射线医师对地面真相分类的比较，对提出的方法进行了定量评估。所提出的方法的性能明显优于其他最新方法，其准确度为86.5％（基准），89.4％（中腔）和84.5％（顶点）。总体分类准确率为87.1％。此外，所提出的方法与放射学家对视觉壁运动评分之间的标准kappa统计比较表明，所提出的算法可以产生0.73的kappa量度。

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