Abstract
Abstract
Background
Beta amyloid in the brain, which was originally confirmed by post-mortem examinations, can now be confirmed in living patients using amyloid positron emission tomography (PET) tracers, and the accuracy of diagnosis can be improved by beta amyloid plaque confirmation in patients. Amyloid deposition in the brain is often associated with the expression of dementia. Hence, it is important to identify the anatomically and functionally meaningful areas of the human brain cortex surface using PET to diagnose the possibility of developing dementia. In this study, we demonstrated the validity of automated 18F-flutemetamol PET lesion detection and segmentation based on a complete 2D U-Net convolutional neural network via masking treatment strategies.
Methods
PET data were first normalized by volume and divided into five amyloid accumulation zones through axial, coronary, and thalamic slices. A single U-Net was trained using a divided dataset for one of these zones. Ground truth segmentations were obtained by manual delineation and thresholding (1.5 × background).
Results
The following intersection over union values were obtained for the various slices in the verification dataset: frontal lobe axial/sagittal: 0.733/0.804; posterior cingulate cortex and precuneus coronal/sagittal: 0.661/0.726; lateral temporal lobe axial/coronal: 0.864/0.892; parietal lobe axial/coronal: 0.542/0.759; and striatum axial/sagittal: 0.679/0.752. The U-Net convolutional neural network architecture allowed fully automated 2D division of the 18F-flutemetamol PET brain images of Alzheimer's patients.
Conclusions
As dementia should be tested and evaluated in various ways, there is a need for artificial intelligence programs. This study can serve as a reference for future studies using auxiliary roles and research in Alzheimer's diagnosis.
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology
Reference39 articles.
1. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 770–8.
2. Kim BS, Lee IH. Retinal blood vessel segmentation using deep learning. J Korean Inst Inf Technol. 2019;17:77–82.
3. Kim YJ, Park SJ, Kim KR, Kim KG. Automated ulna and radius segmentation model based on deep learning on DEXA. J Kor Multimed Soc. 2018;21:1407–16.
4. Bloom GS. Amyloid-beta and tau: the trigger and bullet in Alzheimer’s disease pathogenesis. JAMA Neurol. 2014;71:505–8.
5. Park SJ, Kim YG, Park DK, Chung JW, Kim KG. Evaluation of transfer learning in gastroscopy image classification using convolutional neural network. J Biomed Eng Res. 2018;39:213–9.