Microbleed detection in autopsied brains from community-based older adults using microbleed synthesis and deep learning
Grant Nikseresht1, Ashish A. Tamhane2, Carles Javierre-Petit3, Arnold M. Evia2, David A. Bennett2, Julie A. Schneider2, Gady Agam1, and Konstantinos Arfanakis2,3
1Department of Computer Science, Illinois Institute of Technology, Chicago, IL, United States, 2Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States, 3Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
Automated cerebral microbleed (CMB) detection on ex-vivo MRI is key to enabling MRI-pathology studies in large community-based cohorts where manual CMB annotation is time consuming and prone to error. The aim of this study is to develop a CMB detection algorithm to aid in the quantization and localization of CMBs on ex-vivo T2*-weighted gradient echo MRI in community-based cohorts. A CMB synthesis algorithm is proposed and synthetic CMBs are used to train a neural network for CMB detection. A model trained with both synthetic and real data is shown to outperform models trained on synthetic or real data alone.
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