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首页> 外国专利> DEEP CONVOLUTIONAL NEURAL NETWORKS FOR TUMOR SEGMENTATION WITH POSITRON EMISSION TOMOGRAPHY

DEEP CONVOLUTIONAL NEURAL NETWORKS FOR TUMOR SEGMENTATION WITH POSITRON EMISSION TOMOGRAPHY

机译：正电子发射断层扫描的肿瘤分割深卷积神经网络

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摘要

The present disclosure relates to techniques for segmenting tumors with positron emission tomography (PET) using deep convolutional neural networks for image and lesion metabolism analysis. Particularly, aspects of the present disclosure are directed to obtaining a PET scans and computerized tomography (CT) or magnetic resonance imaging (MRI) scans for a subject, preprocessing the PET scans and the CT or MRI scans to generate standardized images, generating two-dimensional segmentation masks, using two-dimensional segmentation models implemented as part of a convolutional neural network architecture that takes as input the standardized images, generating three-dimensional segmentation masks, using three-dimensional segmentation models implemented as part of the convolutional neural network architecture that takes as input patches of image data associated with segments from the two-dimensional segmentation mask, and generating a final imaged mask by combining information from the two-dimensional segmentation masks and the three-dimensional segmentation masks.
机译：本公开涉及使用深卷积神经网络进行正电子发射断层扫描（PET）的分割肿瘤的技术，用于图像和病变代谢分析。特别地，本公开的各方面旨在获得对受试者的PET扫描和计算机断层扫描（CT）或磁共振成像（MRI）扫描，预处理PET扫描和CT或MRI扫描以产生标准化的图像，产生两个 - 尺寸分割掩模，使用作为输入标准化图像的卷积神经网络架构的一部分实现的二维分割模型，使用作为卷积神经网络架构的一部分实现的三维分割模型来生成三维分割掩码。作为与来自二维分割掩模的段相关联的图像数据的输入斑块，并通过组合来自二维分割掩模和三维分割掩模的信息来生成最终成像掩模。

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