Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps. (January 2019)

• Record Type: Journal Article
• Authors: Togo, Ren; Hirata, Kenji; Manabe, Osamu; Ohira, Hiroshi; Tsujino, Ichizo; Magota, Keiichi; Ogawa, Takahiro; Haseyama, Miki; Shiga, Tohru
• Is Part Of: Computers in biology and medicine. Volume 104(2019)
• Page Start: 81
• Page End: 86
• Publication Date: 2019-01
• Subjects: Deep learning -- Convolutional neural network (CNN) -- Cardiac sarcoidosis (CS) -- 18F-FDG PET -- Computer-aided diagnosis -- Radiology -- Machine learning -- Feature extraction -- Feature selection; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.11.008
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: A method for detection of cardiac sarcoidosis was proposed. The DCNN-based features were effective for the cardiac sarcoidosis classification. Feature selection algorithm was effective for improving the performance.