Classification of¹⁸F-Florbetaben Amyloid Brain PET Image using PCA-SVM

Abstract

Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish β-Amyloid (Aβ) positive from Aβ negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). 18 F-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for Aβ positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for Aβ positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify 18 F-Florbetaben amyloid brain PET image for Aβ positivity using PCA-SVM model, with no additional effects on GMM.

Visual and Quantitative Evaluation of Amyloid Brain PET Image Synthesis with Generative Adversarial Network

“…Before elucidating the design of a generative model in Section 2.4, we first defined the target model for which the images created from the generative model in this experiment will be trained. Previous studies [43,44] have shown that the performance of the ML/DL-based classification system for the Aβ distribution on FBB amyloid PET image data obtained from DAUH was 92.38% and 93.37%, respectively. Brain images generated using various modalities such as Magnetic Resonance Imaging (MRI), CT, and PET maintain spatial, and, depending on the conditions, temporal information of more than 3-dimensions.…”

“…When evaluating PET images for the presence of FBB amyloid, the nuclear medicine physician makes a reading decision based on the contrast of gray matter observed through the axial plane of the FBB Aβ PET. Therefore, in a previous study [43,44,46], the BAPL score of a given FBB PET was estimated based on the Aβ distributions found at each axial level, also known as regional cortical tracer uptake (RCTU), instead of extracting the features from the 3D information according to the current process used by physicians.…”

“…In particular, In a case that an input medical image is a originally gray scale (e.g., ultrasonography, MRI, and PET), previous studies which use the conversion of gray to RGB reports feasible performance even with an unknown artifacts and increased complexity [50,51]. Although the input of the target model was FBB PET images which is originally a gray scale version of a real PET image, the channels of the input data were transformed into a color channel using the OpenCV-python library to match the channel size of the finetuned DenseNet model for a continuity and reproducibility of previous studies [43,44,46]. Target model training and model selection validation were performed using 4-fold nested cross-validation and Bayesian optimization for SVM.…”

“…using the OpenCV-python library to match the channel size of the finetuned DenseNet model for a continuity and reproducibility of previous studies [43,44,46]. Target model training and model selection validation were performed using 4-fold nested cross-validation and Bayesian optimization for SVM.…”

Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network

“…Moreover, there is also recent work using them on automatic and semi-automatic segmentation algorithms in PET [ 6 , 7 ]. In one study, an AD diagnosis classifier using PCA and SVM was utilized after image dimension reduction of [ 18 F]Florbetaben brain PET images [ 8 ], and in another, images were classified according to amyloid deposition with an accuracy of 89% [ 9 ] using Visual Geometry Group (VGG) 16 [ 10 ], which is a well-known structure among convolutional neural networks (CNN) [ 11 , 12 ] that specializes in image feature extraction using deep learning technology.…”