Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) scans, we propose a new Computer-Aided Detection (CAD) system using CNNs and CT image segmentation techniques. Unlike former studies focusing on the classification of malignant nodule types or relying on prior image processing, in this work, we put raw CT image patches directly in CNNs to reduce the complexity of the system. Specifically, we split each CT image into several patches, which are divided into 6 types consisting of 3 nodule types and 3 non-nodule types. We compare the performance of ResNet with different CNNs architectures on CT images from a publicly available dataset named the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). Results show that our best model reaches a high detection sensitivity of 92.8% with 8 false positives per scan (FPs/scan). Compared with related work, our work obtains a state-of-the-art effect.

中文翻译：

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使用基于原始补丁的卷积神经网络进行 CT 图像中的肺结节检测。

由于最近对深度卷积神经网络（CNN）的研究，图像分类和分割方面取得了显着的进展。为了解决低剂量计算机断层扫描 (CT) 扫描中诊断性肺结节检测的类似问题，我们提出了一种使用 CNN 和 CT 图像分割技术的新计算机辅助检测 (CAD) 系统。与之前的研究侧重于恶性结节类型的分类或依赖于先前的图像处理不同，在这项工作中，我们将原始 CT 图像块直接放入 CNN 中，以降低系统的复杂性。具体来说，我们将每张 CT 图像分割为多个斑块，这些斑块分为 6 种类型，其中包括 3 种结节类型和 3 种非结节类型。我们比较了 ResNet 与不同 CNN 架构在来自名为肺部图像数据库联盟和图像数据库资源计划 (LIDC-IDRI) 的公开数据集的 CT 图像上的性能。结果表明，我们的最佳模型达到了 92.8% 的高检测灵敏度，每次扫描 (FPs/scan) 出现 8 个误报。与相关工作相比，我们的工作取得了state-of-the-art的效果。