An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans

Liyun Chen, Dongdong Gu, Yanbo Chen, Ying Shao, Xiaohuan Cao, Guocai Liu, Yaozong Gao, Qian Wang, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

Abstract

Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.

Original languageEnglish
Article number101899
JournalComputerized Medical Imaging and Graphics
Volume89
DOIs
Publication statusPublished - 2021 Apr
Externally publishedYes

Keywords

  • Computed tomography (CT)
  • Lung nodule atlas
  • Lung nodule detection
  • Lung nodule segmentation
  • Statistical analysis

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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