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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| Published in: | Journal of Nuclear Cardiology Vol. 30; no. 2; pp. 540 - 549 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.04.2023
Elsevier BV Springer International Publishing Springer Nature B.V |
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| ISSN: | 1071-3581, 1532-6551, 1532-6551 |
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| Abstract | 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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| AbstractList | 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. 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.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.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.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.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%.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%.The results indicated the utility of unsupervised feature learning for CBIR in MPI.CONCLUSIONThe results indicated the utility of unsupervised feature learning for CBIR in MPI. 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. Background 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. Methods 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. Results 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%. Conclusion The results indicated the utility of unsupervised feature learning for CBIR in MPI. Graphical abstract 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. ▪ |
| Author | Kawamura, Go Aono, Tetsuya Kurokawa, Tsukasa Matsuda, Kensho Kawada, Yoshitaka Hosokawa, Saki Shigematsu, Tatsuya Higaki, Akinori Okabe, Hikaru Kosaki, Tetsuya Kazatani, Takuro Hiasa, Go Tanaka, Yuta Yamada, Tadakatsu Okayama, Hideki Kido, Shinsuke Kawaguchi, Naoto |
| Author_xml | – sequence: 1 givenname: Akinori surname: Higaki fullname: Higaki, Akinori email: keroplant83@gmail.com organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 2 givenname: Naoto surname: Kawaguchi fullname: Kawaguchi, Naoto organization: Department of Radiology, Ehime University Graduate School of Medicine, Toon, Japan – sequence: 3 givenname: Tsukasa surname: Kurokawa fullname: Kurokawa, Tsukasa organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 4 givenname: Hikaru surname: Okabe fullname: Okabe, Hikaru organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 5 givenname: Takuro surname: Kazatani fullname: Kazatani, Takuro organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 6 givenname: Shinsuke surname: Kido fullname: Kido, Shinsuke organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 7 givenname: Tetsuya surname: Aono fullname: Aono, Tetsuya organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 8 givenname: Kensho surname: Matsuda fullname: Matsuda, Kensho organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 9 givenname: Yuta surname: Tanaka fullname: Tanaka, Yuta organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 10 givenname: Saki surname: Hosokawa fullname: Hosokawa, Saki organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 11 givenname: Tetsuya surname: Kosaki fullname: Kosaki, Tetsuya organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 12 givenname: Go surname: Kawamura fullname: Kawamura, Go organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 13 givenname: Tatsuya surname: Shigematsu fullname: Shigematsu, Tatsuya organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 14 givenname: Yoshitaka surname: Kawada fullname: Kawada, Yoshitaka organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 15 givenname: Go surname: Hiasa fullname: Hiasa, Go organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 16 givenname: Tadakatsu surname: Yamada fullname: Yamada, Tadakatsu organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan – sequence: 17 givenname: Hideki surname: Okayama fullname: Okayama, Hideki organization: Department of Cardiology, Ehime Prefectural Central Hospital, 83, Kasuga-machi, 790-0024, Matsuyama, Japan |
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| Keywords | SSS SDS SRS CAE MPI CAD CBIR SPECT CHD image interpretation ML PCA |
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| Snippet | Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) plays a crucial role in the optimal treatment strategy for patients with... Background Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) plays a crucial role in the optimal treatment strategy for... BackgroundSingle-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) plays a crucial role in the optimal treatment strategy for... |
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| SubjectTerms | 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 |
| Title | Content-based image retrieval for the diagnosis of myocardial perfusion imaging using a deep convolutional autoencoder |
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