Hunan University, Changsha, Hunan, China; Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
Hunan University, Changsha, Hunan, China.
Comput Med Imaging Graph. 2021 Apr;89:101886. doi: 10.1016/j.compmedimag.2021.101886. Epub 2021 Feb 24.
Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.
计算机断层扫描（CT）筛查是早期发现肺癌的有效方法，以提高这种致命疾病的存活率。二十多年来，结节检测、分割和分类等图像处理技术已经得到了广泛的研究，以帮助医生从数百张 CT 切片中识别结节，自动测量结节的形状和 HU 分布，并区分其恶性程度。得益于新的并行计算、多层卷积、非线性池化操作和大数据学习策略，深度学习算法的最新发展在肺结节筛查和计算机辅助诊断（CADx）应用中取得了巨大的进展，因为它们具有高灵敏度和低假阳性率。本文对基于深度学习的肺结节筛查和分析技术进行了综述，重点介绍了它们的性能和临床应用，旨在帮助更好地理解肺结节分析的当前性能、局限性和未来趋势。
