KLI

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

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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.
Author(s)
김남국김은영박범희배현진서준범이상민황혜전
Issued Date
2021
Type
Article
Keyword
Content-based image retrievalConvolutional neural networkCryptogenic Organizing Pneumonia - diagnosisDatabasesFactualDiagnosisDifferentialHumansIdiopathic Interstitial Pneumonias - diagnosisImage ProcessingComputer-AssistedInterstitial lung diseaseMultidetector computed tomographyNeural NetworksComputerRetrospective StudiesThoracic ImagingThorax - diagnostic imagingTomographyX-Ray Computed - methods방사선과학
DOI
10.3348/kjr.2020.0603
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7856
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_nrf_kci_oai_kci_go_kr_ARTI_9701637&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Content-Based%20Image%20Retrieval%20of%20Chest%20CT%20with%20Convolutional%20Neural%20Network%20for%20Diffuse%20Interstitial%20Lung%20Disease:%20Performance%20Assessment%20in%20Three%20Major%20Idiopathic%20Interstitial%20Pneumonias&offset=0&pcAvailability=true
Publisher
KOREAN JOURNAL OF RADIOLOGY
Location
대한민국
Language
영어
ISSN
1229-6929
Citation Volume
22
Citation Number
2
Citation Start Page
281
Citation End Page
290
Appears in Collections:
Medicine > Medicine
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