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Multi-Indices Quantification for Left Ventricle via DenseNet and GRU-Based Encoder-Decoder with Attention
Complexity ( IF 2.3 ) Pub Date : 2021-02-20 , DOI: 10.1155/2021/3260259
Zhi Liu 1 , Yunhua Lu 1 , Xiaochuan Zhang 1 , Sen Wang 1 , Shuo Li 2, 3 , Bo Chen 4
Affiliation  

More and more research on left ventricle quantification skips segmentation due to its requirement of large amounts of pixel-by-pixel labels. In this study, a framework is developed to directly quantify left ventricle multiple indices without the process of segmentation. At first, DenseNet is utilized to extract spatial features for each cardiac frame. Then, in order to take advantage of the time sequence information, the temporal feature for consecutive frames is encoded using gated recurrent unit (GRU). After that, the attention mechanism is integrated into the decoder to effectively establish the mappings between the input sequence and corresponding output sequence. Simultaneously, a regression layer with the same decoder output is used to predict multi-indices of the left ventricle. Different weights are set for different types of indices based on experience, and l2-norm is used to avoid model overfitting. Compared with the state-of-the-art (SOTA), our method can not only produce more competitive results but also be more flexible. This is because the prediction results in our study can be obtained for each frame online while the SOTA only can output results after all frames are analyzed.

中文翻译:

通过DenseNet和基于GRU的编码器-解码器注意左心室的多指标量化

由于需要大量的逐个像素标签,因此越来越多的关于左心室量化的研究跳过了分割。在这项研究中,开发了一个框架,可以直接量化左心室多个指标,而无需分割过程。首先,利用DenseNet为每个心脏框架提取空间特征。然后,为了利用时序信息,使用门控循环单元(GRU)对连续帧的时间特征进行编码。之后,将注意力机制集成到解码器中,以有效地建立输入序列与对应的输出序列之间的映射。同时,具有相同解码器输出的回归层用于预测左心室的多个指标。根据经验为不同类型的索引设置不同的权重,并且使用l2-范数来避免模型过度拟合。与最新技术(SOTA)相比,我们的方法不仅可以产生更具竞争力的结果,而且可以更加灵活。这是因为我们研究的预测结果可以在线获取每个帧,而SOTA只能在分析所有帧之后输出结果。
更新日期:2021-02-21
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