A myocardial extraction method using deep learning for 99mTc myocardial perfusion SPECT images: A basic study to reduce the effects of extra-myocardial activity.

Kikuchi, Akihiro; Wada, Naofumi; Kawakami, Takashi; Nakajima, Kenichi; Yoneyama, Hiroto

Kikuchi, Akihiro; Wada, Naofumi; Kawakami, Takashi; Nakajima, Kenichi; Yoneyama, Hiroto.

Afiliación

Kikuchi A; Department of Radiological Technology, Faculty of Health Sciences, Hokkaido University of Science, Japan. Electronic address: kikuchi-a@hus.ac.jp.
Wada N; Department of Information and Computer Science, Faculty of Engineering, Hokkaido University of Science, Japan.
Kawakami T; Department of Information and Computer Science, Faculty of Engineering, Hokkaido University of Science, Japan.
Nakajima K; Department of Functional Imaging and Artificial Intelligence, Kanazawa University, Japan.
Yoneyama H; Kanazawa University Hospital, Kanazawa, Japan.

Comput Biol Med ; 141: 105164, 2022 02.

Article en En | MEDLINE | ID: mdl-34971980

Asunto(s)

Aprendizaje Profundo; Redes Neurales de la Computación; Perfusión; Tomografía Computarizada de Emisión de Fotón Único

Palabras clave

99mTc-tetrofosmin; Convolutional neural network (CNN); Deep learning; Extracardiac activity; Feature extraction; Tc-99m-sestamibi; U-Net; U-Net++

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Idioma: En Revista: Comput Biol Med Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos

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