Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps

Togo, Ren; Hirata, Kenji; Manabe, Osamu; Ohira, Hiroshi; Tsujino, Ichizo; Magota, Keiichi; Ogawa, Takahiro; Haseyama, Miki; Shiga, Tohru

Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps

Togo, Ren; Hirata, Kenji; Manabe, Osamu et al.

Computers in Biology and Medicine, 2019, 104, 81-86

Permalink : https://hdl.handle.net/2115/76454

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アクセス権	open access
URI	https://hdl.handle.net/2115/76454
タイトル	Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps
著者	Togo, Ren ; Hirata, Kenji ; Manabe, Osamu ; Ohira, Hiroshi ; Tsujino, Ichizo ; Magota, Keiichi ; Ogawa, Takahiro ; Haseyama, Miki ; Shiga, Tohru
著者名 Togo, Ren
e-Rad_Researcher 30431365 著者名 Hirata, Kenji
e-Rad_Researcher 40443957 著者名 Manabe, Osamu
e-Rad_Researcher 20528301 著者名 Ohira, Hiroshi
e-Rad_Researcher 00344507 著者名 Tsujino, Ichizo
e-Rad_Researcher 20636419 著者名 Magota, Keiichi
e-Rad_Researcher 20524028 著者名 Ogawa, Takahiro
e-Rad_Researcher 00218463 著者名 Haseyama, Miki
e-Rad_Researcher 80374495 著者名 Shiga, Tohru
言語	英語
キーワード	Deep learning
	Convolutional neural network (CNN)
	Cardiac sarcoidosis (CS)
	18F-FDG PET
	Computer-aided diagnosis
	Radiology
	Machine learning
	Feature extraction
	Feature selection
発行日	2019-01
出版者	Elsevier
収録物名	Computers in Biology and Medicine
巻(号)	104
ページ	81-86
抄録	Aims: The aim of this study was to determine whether deep convolutional neural network (DCNN)-based features can represent the difference between cardiac sarcoidosis (CS) and non-CS using polar maps.　Methods: A total of 85 patients (33 CS patients and 52 non-CS patients) were analyzed as our study subjects. One radiologist reviewed PET/CT images and defined the left ventricle region for the construction of polar maps. We extracted high-level features from the polar maps through the Inception-v3 network and evaluated their effectiveness by applying them to a CS classification task. Then we introduced the ReliefF algorithm in our method. The standardized uptake value (SUV)-based classification method and the coefficient of variance (CoV)-based classification method were used as comparative methods. Results: Sensitivity, specificity and the harmonic mean of sensitivity and specificity of our method with the ReliefF algorithm were 0.839, 0.870 and 0.854, respectively. Those of the SUVmax-based classification method were 0.468, 0.710 and 0.564, respectively, and those of the CoV-based classification method were 0.655, 0.750 and 0.699, respectively. Conclusion: The DCNN-based high-level features may be more effective than low-level features used in conventional quantitative analysis methods for CS classification.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
権利情報	© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
PISSN	0010-4825
NCID	AA0061348X
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1016/j.compbiomed.2018.11.008