Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
Comput Biol Med. 2021 Oct;137:104806. doi: 10.1016/j.compbiomed.2021.104806. Epub 2021 Aug 25.
Lung cancer has one of the highest mortalities of all cancers. According to the National Lung Screening Trial, patients who underwent low-dose computed tomography (CT) scanning once a year for 3 years showed a 20% decline in lung cancer mortality. To further improve the survival rate of lung cancer patients, computer-aided diagnosis (CAD) technology shows great potential. In this paper, we summarize existing CAD approaches applying deep learning to CT scan data for pre-processing, lung segmentation, false positive reduction, lung nodule detection, segmentation, classification and retrieval. Selected papers are drawn from academic journals and conferences up to November 2020. We discuss the development of deep learning, describe several important aspects of lung nodule CAD systems and assess the performance of the selected studies on various datasets, which include LIDC-IDRI, LUNA16, LIDC, DSB2017, NLST, TianChi, and ELCAP. Overall, in the detection studies reviewed, the sensitivity of these techniques is found to range from 61.61% to 98.10%, and the value of the FPs per scan is between 0.125 and 32. In the selected classification studies, the accuracy ranges from 75.01% to 97.58%. The precision of the selected retrieval studies is between 71.43% and 87.29%. Based on performance, deep learning based CAD technologies for detection and classification of pulmonary nodules achieve satisfactory results. However, there are still many challenges and limitations remaining including over-fitting, lack of interpretability and insufficient annotated data. This review helps researchers and radiologists to better understand CAD technology for pulmonary nodule detection, segmentation, classification and retrieval. We summarize the performance of current techniques, consider the challenges, and propose directions for future high-impact research.
肺癌是所有癌症中死亡率最高的一种。根据国家肺癌筛查试验，每年接受一次低剂量计算机断层扫描（CT）扫描，持续 3 年的患者肺癌死亡率降低了 20%。为了进一步提高肺癌患者的生存率，计算机辅助诊断（CAD）技术显示出巨大的潜力。在本文中，我们总结了现有的 CAD 方法，这些方法将深度学习应用于 CT 扫描数据进行预处理、肺部分割、减少假阳性、肺结节检测、分割、分类和检索。所选论文来自截至 2020 年 11 月的学术期刊和会议。我们讨论了深度学习的发展，描述了肺结节 CAD 系统的几个重要方面，并评估了所选研究在各种数据集上的性能，这些数据集包括 LIDC-IDRI、LUNA16、LIDC、DSB2017、NLST、TianChi 和 ELCAP。总的来说，在所回顾的检测研究中，这些技术的灵敏度发现范围在 61.61%到 98.10%之间，每个扫描的假阳性率在 0.125 到 32 之间。在选定的分类研究中，准确性范围从 75.01%到 97.58%。所选检索研究的精度在 71.43%到 87.29%之间。基于性能，基于深度学习的肺结节检测和分类 CAD 技术取得了令人满意的结果。然而，仍然存在许多挑战和限制，包括过度拟合、缺乏可解释性和数据不足。本综述有助于研究人员和放射科医生更好地理解肺结节检测、分割、分类和检索的 CAD 技术。我们总结了当前技术的性能，考虑了挑战，并提出了未来高影响力研究的方向。
