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BRAIN TISSUE LAYERING METHOD AND DEVICE BASED ON NEURAL NETWORK, AND COMPUTER DEVICE

机译:基于神经网络的脑组织分层方法和装置和计算机装置

摘要

A brain tissue layering method and device based on a neural network, a computer device, and a storage medium, relating to the field of artificial intelligence. The method comprises: obtaining brain CT image information (201); extracting features of the brain CT image information by means of a pre-trained brain cutting convolutional neural network to obtain a feature map of the brain CT image (202); performing a candidate box alignment operation on the feature map of the brain CT image to obtain semantic information of an instance category and location information of an instance pixel level (203); and inputting the semantic information of the instance category and the location information of the instance pixel level into a pre-trained layering neural network, and outputting a layered result of the brain CT image (204). By fusing the brain cutting convolutional neural network and the brain layering neural network, the results of brain cutting and brain layering are obtained simultaneously by using one model, so that the operation time and the consumption of operation resources are reduced, and the tasks of brain cutting and brain layering can share feature information, thereby improving the accuracy of brain layering.
机译:基于神经网络,计算机设备和存储介质的脑组织分层方法和装置,与人工智能领域有关。该方法包括:获得脑CT图像信息(201);通过预先培训的脑切割卷积神经网络提取脑CT图像信息的特征,以获得脑CT图像的特征图(202);对脑CT图像的特征映射执行候选盒对准操作,以获取实例类别的语义信息和实例像素级别的位置信息(203);并将实例类别的语义信息和将实例像素电平的位置信息输入到预训练的分层神经网络中,并输出脑CT图像的分层结果(204)。通过融合脑切割卷积神经网络和脑分层神经网络,通过使用一个模型同时获得脑切割和脑分层的结果,使操作时间和运营资源的消耗减少,以及大脑的任务切割和脑分层可以共享特征信息,从而提高脑分层的准确性。

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