VGG-based BAPL Score Classification of 18F-Florbetaben Amyloid Brain PET

Abstract

Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the β-Amyloid (Aβ) deposition. We designed a convolutional neural network (CNN) model that predicts the Aβ-positive and Aβ-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patientbased standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the Aβ-positive and Aβ-negative status.

Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function †

“…The analyzed data were PET images provided by Dong-A University of Korea [12]. All participants underwent 18F FBB PET/CT.…”

“…In image classification, by the existing deep-learning-based AD classification methods [3,[9][10][11][12][13], a common loss function is the cross entropy. In this paper, we first defined the cross-entropy as the following inter-loss function:…”

“…Liu [13] combined a CNN with recurrent neural networks (RNN) for classifying amyloids in fluorodeoxyglucose (FDG) PET images. Although Kang's method satisfactorily classified images as positive or negative for amyloids, it could not accurately identify BAPL2 in a ternary classification, because BAPL2 is a weaker amyloid load than BAPL1 [12]. In some cases, the interpreter cannot easily distinguish between BAPL1 and BAPL2.…”

“…This failure might be attributable to the late application of the treatment, highlighting the need for early treatment after early diagnosis [5]. Amyloid markers are the fastest appearing biomarkers early in the disease [6][7][8].In recent years, AD in MRI or PET brain images has been identified by various machine learning methods including deep learning [3,[9][10][11][12][13]. Zhang [9] classified AD and normal control images by a combined kernel technique with a support vector machine.…”

“…In recent years, AD in MRI or PET brain images has been identified by various machine learning methods including deep learning [3,[9][10][11][12][13]. Zhang [9] classified AD and normal control images by a combined kernel technique with a support vector machine.…”