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COMPUTED TOMOGRAPHY PULMONARY NODULE DETECTION METHOD BASED ON DEEP LEARNING

机译:基于深度学习的计算机X线摄影肺结节检测方法

摘要

A computed tomography (CT) pulmonary nodule detection method based on deep learning is provided. The method comprises the steps of: acquiring 3D pulmonary CT sequence images of a user; processing the acquired 3D pulmonary CT sequence images into 2D image data; inputting 2D image data into a preset deep learning network model for training to obtain a trained pulmonary nodule detection model; inputting a set of 3D pulmonary CT sequence images to be tested into the trained pulmonary nodule detection model to obtain a preliminary pulmonary nodule detection resu applying a pulmonary region segmentation algorithm based on deep learning to the preliminary pulmonary nodule detection result to remove false positive pulmonary nodules, so as to obtain a final pulmonary nodule detection result.
机译:提供了一种基于深度学习的计算机断层扫描(CT)肺结节检测方法。该方法包括以下步骤:获取用户的3D肺部CT序列图像;以及将获取的3D肺部CT序列图像处理为2D图像数据;将二维图像数据输入预设的深度学习网络模型中进行训练以获得训练后的肺结节检测模型;将一组待测试的3D肺CT序列图像输入训练后的肺结节检测模型,以获得初步的肺结节检测结果;将基于深度学习的肺区域分割算法应用于初步的肺结节检测结果,去除假阳性肺结节,从而获得最终的肺结节检测结果。

著录项

  • 公开/公告号US2019287242A1

    专利类型

  • 公开/公告日2019-09-19

    原文格式PDF

  • 申请/专利权人 INFERVISION;

    申请/专利号US201916351896

  • 申请日2019-03-13

  • 分类号G06T7;G06N3/08;G06N20;

  • 国家 US

  • 入库时间 2022-08-21 12:12:02

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