Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship Learning

机译：使用基于深层聚合的多任务关系学习左心室全量化

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Left ventricle full quantification is important in the assessment of cardiac functionality and diagnosis of cardiac diseases, but is also challenging due to the sample variability and label correlations. In this paper, we propose a deep-learning based approach for left ventricle full quantification, including 11 indices regression and cardiac phase recognition. We utilize Deep Layer Aggregation as backbone, perform 11 indices regression simultaneously supervised by multitask relationship loss, and then derive the cardiac phase by searching maximum and minimum frame from polynomial-fitted cavity area. Experiments demonstrate the superiority of the proposed method in performance.

机译：左心室全量化在评估心脏功能和心脏病诊断方面是重要的，但由于样品可变性和标签相关性也是挑战性的。在本文中，我们提出了一种基于深度学习的左心室全量化方法，包括11个指数回归和心脏阶段识别。我们利用深层聚合作为骨干，通过多任务关系丢失同时监督11个索引回归，然后通过从多项式腔区域搜索最大和最小帧来导出心脏阶段。实验证明了所提出的性能方法的优越性。

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《International Workshop on Statistical Atlases and Computational Models of the Heart》|2019年|487p|共8页
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Jiahui Li; Zhiqiang Hu;
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中图分类 TP391.41-53;
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Left ventricle full quantification; Deep Layer Aggregation; Multitask relationship learning; Polynomial fitting;

机译：左心室全量化;深层聚合;多任务关系学习;多项式配件;

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专利

1. Full left ventricle quantification via deep multitask relationships learning [J] . Wufeng Xue, Gary Brahm, Sachin Pandey, Medical image analysis . 2018,第期

机译：通过深度多任务关系学习全左心室量化
2. A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images [J] . Abdeltawab Hisham, Khalifa Fahmi, Taher Fatma, Computerized Medical Imaging and Graphics: The Official Jounal of the Computerized Medical Imaging Society . 2020,第期

机译：基于深度学习的自动分割和量化心脏CINE MR图像的左心室的定量方法
3. Direct Segmentation-Based Full Quantification for Left Ventricle via Deep Multi-Task Regression Learning Network [J] . Du Xiuquan, Tang Renjun, Yin Susu, Biomedical and Health Informatics, IEEE Journal of . 2019,第3期

机译：通过直接的基于分割的深度多任务回归学习网络对左心室进行全定量
4. Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship Learning [C] . Jiahui Li, Zhiqiang Hu International Workshop on Statistical Atlases and Computational Models of the Heart . 2019

机译：使用基于深层聚合的多任务关系学习左心室全量化
5. Pumping potential of a two-layer left-ventricle-like flexible matrix composite (fmc) structure [D] . Chanda, Arnab 2014

机译：两层左心室状柔性基质复合材料（fmc）结构的泵浦电位
6. Infarcted Left Ventricles Have Stiffer Material Properties and Lower Stiffness Variation: Three-Dimensional Echo-Based Modeling to Quantify In Vivo Ventricle Material Properties [O] . Longling Fan, Jing Yao, Chun Yang, -1

机译：梗死的左心室具有更硬的材料特性和较低的刚度变化：基于三维回波的建模来量化体内心室的材料特性
7. Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness [O] . Xue, Wufeng, Lum, Andrea, Mercado, Ashley, 2017

机译：通过深度多任务学习对左心室进行全量化网络尊重内部和任务间相关性

Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship Learning

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