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Abstract #2824

Ultrafast Brain Segmentation using a 3D Fully Convolutional Neural Network for Risk Stratification of Cognitive Impairment and Dementia

Jian Wu1, Alex Graff1, Jason Deckman1, Dmitry Tkach1, Hyun-Kyung Chung1, Natalie M Schenker-Ahmed1, David Karow1, and Christine Leon Swisher1

1Human Longevity, Inc., San Diego, CA, United States

Early detection and intervention has the potential for slowing disease progression of dementia. Brain segmentation of T1W structural MRI is an effective biomarker for assessing cognitive decline. However, automatic brain segmentation has required lengthy processing time and utilizes single structure for risk stratification. In this study, we developed a 3D fully convolutional neural network for ultrafast brain segmentation. Both qualitative and quantitative analysis demonstrated that our segmentation method has a strong generalization capability achieving promising experimental results. Furthermore, we utilized multiple regions in combination and defined a new biomarker to better differentiate early disease progression of normal versus MCI in addition to MCI versus Dementia. Our multi-region approach outperforms conventional single biomarkers.

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