The LISS - A public database of common imaging signs of lung diseases for computer-aided detection and diagnosis research and medical education

Guanghui Han, Xiabi Liu, Feifei Han, I. Nyoman Tenaya Santika, Yanfeng Zhao, Xinming Zhao*, Chunwu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)

Abstract

Lung computed tomography (CT) imaging signs play important roles in the diagnosis of lung diseases. In this paper, we review the significance of CT imaging signs in disease diagnosis and determine the inclusion criterion of CT scans and CT imaging signs of our database. We develop the software of abnormal regions annotation and design the storage scheme of CT images and annotation data. Then, we present a publicly available database of lung CT imaging signs, called LISS for short, which contains 271 CT scans and 677 abnormal regions in them. The 677 abnormal regions are divided into nine categories of common CT imaging signs of lung disease (CISLs). The ground truth of these CISLs regions and the corresponding categories are provided. Furthermore, to make the database publicly available, all private data in CT scans are eliminated or replaced with provisioned values. The main characteristic of our LISS database is that it is developed from a new perspective of CT imaging signs of lung diseases instead of commonly considered lung nodules. Thus, it is promising to apply to computer-aided detection and diagnosis research and medical education.

Original languageEnglish
Article number6924794
Pages (from-to)648-656
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume62
Issue number2
DOIs
Publication statusPublished - 1 Feb 2015

Keywords

  • CT imaging signs
  • Computer-aided diagnosis (CAD)
  • lung lesions
  • medical database
  • medical education

Fingerprint

Dive into the research topics of 'The LISS - A public database of common imaging signs of lung diseases for computer-aided detection and diagnosis research and medical education'. Together they form a unique fingerprint.

Cite this