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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-07-08

AUTHORS

Akinori Higaki, Naoto Kawaguchi, Tsukasa Kurokawa, Hikaru Okabe, Takuro Kazatani, Shinsuke Kido, Tetsuya Aono, Kensho Matsuda, Yuta Tanaka, Saki Hosokawa, Tetsuya Kosaki, Go Kawamura, Tatsuya Shigematsu, Yoshitaka Kawada, Go Hiasa, Tadakatsu Yamada, Hideki Okayama

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.Graphical abstract More... »

PAGES

1-10

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12350-022-03030-4

DOI

http://dx.doi.org/10.1007/s12350-022-03030-4

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https://app.dimensions.ai/details/publication/pub.1149335055

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35802346


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62 model
63 myocardial perfusion
64 myocardial perfusion images
65 myocardial perfusion imaging
66 optimal treatment strategy
67 pairs
68 pairs of stress
69 patients
70 percent
71 perfusion
72 perfusion images
73 perfusion imaging
74 plots
75 precision
76 presence
77 presence of ischemia
78 principal component analysis
79 queries
80 radiologists
81 radiologists' findings
82 recall
83 reference image
84 results
85 retrieval
86 role
87 scar
88 scatter plots
89 scintigraphy
90 scores
91 similarity
92 strategies
93 stress
94 three-dimensional scatter plot
95 tomography myocardial perfusion imaging
96 tool
97 treatment strategies
98 unsupervised feature
99 utility
100 vector
101 visualization
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