ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2023
Content-based image retrieval for the diagnosis of myocardial perfusion imaging using a deep convolutional autoencoder.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 7 citations in OpenAlex.
- Variational autoencoders can detect coronary artery disease in myocardial perfusion SPECT images.European journal of nuclear medicine and molecular imaging · 2026Article
- The explainability of the latent variables is limited to the synthesis of electrocardiogram.European heart journal. Digital health · 2022Article
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17 authors at 2 institutions in 1 country.
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Abstract
backgroundSingle-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.
methodsEight 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.
resultsA 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%.
conclusionThe results indicated the utility of unsupervised feature learning for CBIR in MPI.
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