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首页> 外文期刊>Cancer Management and Research >Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network
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Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network

机译：CT图像深度学习图像：使用卷积神经网络进行自动肺结核检测，用于随后的管理

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Purpose: The?purpose?of?this?study?is?to compare the detection performance of the 3-dimensional convolutional neural network (3D CNN)-based computer-aided detection (CAD) models with radiologists of different levels of experience in detecting pulmonary nodules on thin-section computed tomography (CT). Patients and Methods: We retrospectively reviewed 1109 consecutive patients who underwent follow-up thin-section CT at our institution. The 3D CNN model for nodule detection was re-trained and complemented by expert augmentation. The annotations of a consensus panel consisting of two expert radiologists determined the ground truth. The detection performance of the re-trained CAD model and three other radiologists at different levels of experience were tested using a free-response receiver operating characteristic (FROC) analysis in the test group. Results: The detection performance of the re-trained CAD model was significantly better than that of the pre-trained network (sensitivity: 93.09% vs 38.44%). The re-trained CAD model had a significantly better detection performance than radiologists (average sensitivity: 93.09% vs 50.22%), without significantly increasing the number of false positives per scan (1.64 vs 0.68). In the training set, 922 nodules less than 3 mm in size in 211 patients at high risk were recommended for follow-up CT according to the Fleischner Society Guidelines. Fifteen of 101 solid nodules were confirmed to be lung cancer. Conclusion: The re-trained 3D CNN-based CAD model, complemented by expert augmentation, was an accurate and efficient tool in identifying incidental pulmonary nodules for subsequent management.
机译：目的：？目的？这是什么？研究？是吗？用于比较三维卷积神经网络（3D CNN）的检测性能，基于计算机辅助检测（CAD）模型，具有不同程度的检测体验的辐射学薄截面计算断层扫描（CT）的肺结节。患者及方法：我们回顾性地审查了1109名连续患者，在我们的机构接受了后续薄型CT。用于结节检测的3D CNN模型被重新培训并通过专家增强辅验。由两个专家放射科医生组成的共识面板的注释确定了基础事实。使用试验组的自由响应接收器操作特征（FROC）分析测试重新训练的CAD模型和三位其他放射科医生的检测性能和三个其他放射科医生。结果：重新培训的CAD模型的检测性能明显优于预先培训的网络（敏感性：93.09％VS 38.44％）。重新训练的CAD模型具有比放射科学家（平均灵敏度：93.09％VS 50.22％）具有明显更好的检测性能，而不会显着增加每次扫描的误报的数量（1.64 Vs 0.68）。在训练套装中，根据Fleischner社会指导，建议在高风险中占211例高风险的922名低于3毫米的结节。确认101个固体结节的十五种肺癌。结论：专业增强的重新培训3D CNN的CAD模型是一种准确和有效的工具，用于识别后续管理的偶然肺结节。

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