Content-based image retrieval for the diagnosis of myocardial perfusion imaging using a deep convolutional autoencoder

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) plays a crucial role in the optimal treatment strategy for patients with coronary heart disease. We tested the feasibility of feature extraction from MPI using a deep convolutional autoencoder (CAE) model. Eight hu...

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Bibliographic Details
Published in:Journal of Nuclear Cardiology Vol. 30; no. 2; pp. 540 - 549
Main Authors: Higaki, Akinori, Kawaguchi, Naoto, Kurokawa, Tsukasa, Okabe, Hikaru, Kazatani, Takuro, Kido, Shinsuke, Aono, Tetsuya, Matsuda, Kensho, Tanaka, Yuta, Hosokawa, Saki, Kosaki, Tetsuya, Kawamura, Go, Shigematsu, Tatsuya, Kawada, Yoshitaka, Hiasa, Go, Yamada, Tadakatsu, Okayama, Hideki
Format: Journal Article
Language:English
Published: Cham Elsevier Inc 01.04.2023
Elsevier BV
Springer International Publishing
Springer Nature B.V
Subjects:
CAD
Cardiology
Cardiovascular disease
Coronary Artery Disease
Coronary Artery Disease - diagnosis
Data visualization
Heart
Humans
image interpretation
Image retrieval
Imaging
Medical diagnosis
Medicine
Medicine & Public Health
MPI
Myocardial Perfusion Imaging
Myocardial Perfusion Imaging - methods
Neural Networks, Computer
Nuclear Medicine
Original Article
Radiology
SPECT
Tomography, Emission-Computed, Single-Photon
Tomography, Emission-Computed, Single-Photon - methods
SSS
SDS
SRS
CAE
MPI
CAD
CBIR
SPECT
CHD
image interpretation
ML
PCA
ISSN:1071-3581, 1532-6551, 1532-6551
Online Access:Get full text
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Summary:Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) plays a crucial role in the optimal treatment strategy for patients with coronary heart disease. We tested the feasibility of feature extraction from MPI using a deep convolutional autoencoder (CAE) model. Eight hundred and forty-three pairs of stress and rest myocardial perfusion images were collected from consecutive patients who underwent cardiac scintigraphy in our hospital between December 2019 and February 2022. We trained a CAE model to reproduce the input paired image data, so as the encoder to output a 256-dimensional feature vector. The extracted feature vectors were further dimensionally reduced via principal component analysis (PCA) for data visualization. Content-based image retrieval (CBIR) was performed based on the cosine similarity of the feature vectors between the query and reference images. The agreement of the radiologist’s finding between the query and retrieved MPI was evaluated using binary accuracy, precision, recall, and F1-score. A three-dimensional scatter plot with PCA revealed that feature vectors retained clinical information such as percent summed difference score, presence of ischemia, and the location of scar reported by radiologists. When CBIR was used as a similarity-based diagnostic tool, the binary accuracy was 81.0%. The results indicated the utility of unsupervised feature learning for CBIR in MPI. ▪
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ISSN:1071-3581
1532-6551
1532-6551
DOI:10.1007/s12350-022-03030-4