A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning

Yu Gu; Jingqian Chi; Jiaqi Liu; Lidong Yang; Baohua Zhang; Dahua Yu; Ying Zhao; Xiaoqi Lu

doi:10.1016/j.compbiomed.2021.104806

A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning

Comput Biol Med. 2021 Oct:137:104806. doi: 10.1016/j.compbiomed.2021.104806. Epub 2021 Aug 25.

Authors

Yu Gu¹, Jingqian Chi², Jiaqi Liu³, Lidong Yang³, Baohua Zhang³, Dahua Yu³, Ying Zhao³, Xiaoqi Lu⁴

Affiliations

¹ 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. Electronic address: guyu2010023@imust.edu.cn.
² 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. Electronic address: chi_jingqian@163.com.
³ 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.
⁴ 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; College of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China.

PMID: 34461501
DOI: 10.1016/j.compbiomed.2021.104806

Abstract

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.

Keywords: CT images; Computer-aided diagnosis (CAD) technology; Deep learning; Lung cancer; Lung nodule classification; Lung nodule detection.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Computers
Deep Learning*
Humans
Lung
Lung Neoplasms* / diagnostic imaging
Radiographic Image Interpretation, Computer-Assisted
Sensitivity and Specificity
Solitary Pulmonary Nodule* / diagnostic imaging
Tomography, X-Ray Computed