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首页> 外文会议>International Conference on Medical Image Computing and Computer-Assisted Intervention >Whole Heart and Great Vessel Segmentation in Congenital Heart Disease Using Deep Neural Networks and Graph Matching
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Whole Heart and Great Vessel Segmentation in Congenital Heart Disease Using Deep Neural Networks and Graph Matching

机译：使用深度神经网络和图匹配的先天性心脏病全心脏和大血管分割

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Congenital heart disease (CHD) is the leading cause of mortality from birth defects, which occurs 1 in every 110 births in the United States. While various whole heart and great vessel segmentation frameworks have been developed in the literature, they are ineffective when applied to medical images in CHD, which have significant variations in heart structure and great vessel connections. To address the challenge, we leverage the power of deep learning in processing regular structures and that of graph algorithms in dealing with large variations, and propose a framework that combines both for whole heart and great vessel segmentation in CHD. Particularly, we first use deep learning to segment the four chambers and myocardium followed by blood pool, where variations are usually small. We then extract the connection information and apply graph matching to determine the categories of all the vessels. Experimental results using 68 3D CT images covering 14 types of CHD show that our method can increase Dice score by 12% on average compared with the state-of-the-art whole heart and great vessel segmentation method in normal anatomy. Our dataset is released to the public.
机译：先天性心脏病（CHD）是出生缺陷导致死亡的主要原因，在美国，每110例出生中就有1例发生。尽管在文献中已经开发了各种全心脏和大血管分割框架，但是当将其应用于冠心病中的医学图像时，它们是无效的，冠心病在心脏结构和大血管连接方面有很大的差异。为了应对这一挑战，我们利用深度学习的能力来处理常规结构，并利用图算法来处理较大的变化，并提出了一个框架，该框架既可用于CHD的全心分割又可用于大血管分割。特别是，我们首先使用深度学习对四个腔和心肌进行分割，然后对血池进行细分，血池之间的变化通常很小。然后，我们提取连接信息并应用图匹配来确定所有容器的类别。使用涵盖14种冠心病的68张3D CT图像进行的实验结果表明，与最新的全心脏和正常解剖学中的大血管分割方法相比，我们的方法可将Dice评分平均提高12％。我们的数据集已公开发布。

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