Korean J Radiol.  2021 Feb;22(2):281-290. 10.3348/kjr.2020.0603.

Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias

Affiliations
  • 1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Abstract


Objective
To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD).
Materials and Methods
The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1–5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease).
Results
The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1–5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002). On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5.
Conclusion
The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.

Keyword

Content-based image retrieval; Multidetector computed tomography; Convolutional neural network; Interstitial lung disease
Full Text Links
  • KJR
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2021 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr