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Systems and methods for multi-label segmentation of cardiac computed tomography and angiography images using deep neural networks

机译:使用深神经网络的心脏计算机断层扫描和血管造影图像多标记分割的系统和方法

摘要

Methods and systems are provided for detecting coronary lesions in 3D cardiac computed tomography and angiography (CCTA) images using deep neural networks. In an exemplary embodiment, a method for detecting coronary lesions in 3D CCTA images comprises, acquiring a 3D CCTA image of a coronary tree, mapping the 3D CCTA image to a multi-label segmentation map with a trained deep neural network, generating a plurality of 1D parametric curves for a branch of the coronary tree using the multi-label segmentation map, determining a location of a lesion in the branch of the coronary tree using the plurality of 1D parametric curves, and determining a severity score for the lesion based on the plurality of 1D parametric curves.
机译:使用深神经网络检测3D心脏计算机断层扫描和血管造影(CCTA)图像中的冠状动脉病变检测方法和系统。 在示例性实施例中,用于检测3D CCTA图像中的冠状动脉病变的方法包括,获取冠状动脉树的3D CCTA图像,用训练的深神经网络将3D CCTA图像映射到多标签分段映射,产生多个 1D使用多标签分割图的冠状动脉树的分支的参数曲线,使用多个1D参数曲线确定冠状动脉树的分支中的病变的位置,并基于以下确定病变的严重性分数。 多个1D参数曲线。

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