A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection.
A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection.
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DOI：
10.3390/s21113693
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发表时间：
2021-05-26
期刊：
影响因子：
--
通讯作者：
Niu Y
中科院分区：
文献类型：
--
作者：
Wang X;Wang F;Niu Y
心脏 MRI 左心室 (LV) 检测经常用于协助心脏疾病的计算机辅助诊断中的心脏配准或分割。针对左心室检测中的挑战性问题，如MRI中左心室区域跨度大、大小不一，以及左心室区域心肌和血池部分的异质性，本文提出了一种结合判别字典学习和序列跟踪的卷积神经网络（CNN）检测方法。为了有效地表示 LV 区域中的不同子对象，该方法部署判别字典对超像素过分割区域进行分类，然后通过标签合并构建目标 LV 区域，并在目标区域中生成多尺度自适应锚点以处理不同的尺寸。结合区域提议网络中的非差分锚点，通过基于 CNN 的回归和分类策略来定位左心室对象。为了解决判别词典分类速度慢的问题，还提出了基于序列跟踪的同一个体的左心室尺度自适应锚点快速生成模块。该方法及其变体在心脏图谱数据集上进行了测试。实验结果验证了该方法的有效性，根据一些评价指标，其AP50指标达到了92.95%，与典型的相关方法相比是最具竞争力的结果。判别字典学习和尺度自适应锚的结合提高了所提出的算法对不同左心室区域的适应性。这项研究将有益于一些心脏图像处理，例如感兴趣区域裁剪和左心室容积测量。
Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.
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影响因子：
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影响因子：
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