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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：利用深神经网络和图形匹配先天性心脏病的全心和大血管分割

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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）是来自出生缺陷的死亡率的主要原因，在美国每110名110名出生中发生1。虽然在文献中已经开发了各种整个心脏和巨大的船只分割框架，但在CHD中应用于医学图像时，它们无效，这在心脏结构和大容器连接中具有显着变化。为了应对这一挑战，我们利用深度学习的动力在处理常规结构和的图形算法在处理大的变化，并提出了一个框架，既为整个心脏和冠心病，大血管分割联合收割机。特别是，我们首先使用深入学习来分割四个室和心肌，然后进行血液池，其中变化通常很小。然后，我们提取连接信息并应用图形匹配以确定所有船只的类别。使用68个3D CT图像的实验结果涵盖14种CHD，表明，与正常解剖学的最新全心脏和大血管分割方法相比，我们的方法可以平均增加12％的骰子得分。我们的数据集已发布给公众。

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