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Abstract #0028

Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks

Maged Goubran1, Edward Ntiri1, Hassan Akhavein1, Melissa Holmes1, Sean Nestor1, Ramirez Joel1, Sabrina Adamo1, Fuqiang Gao1, Christopher Scott1, Anne Martel1, Walter Swardfager1, Mario Masellis1, Rick Swartz1, Bradley MacIntosh1, and Sandra Black1

1Sunnybrook Research Institute, Toronto, ON, Canada

Obtaining hippocampal volumes through manual segmentation requires an expert and is time consuming. Automated segmentation techniques would benefit from user-friendly and publicly accessible to tools, and robust results in the face of brain diseases. To accomplish these objectives, we trained a 3D convolutional neural network to segment the hippocampus automatically. Our algorithm was more accurate and time efficient compared to 4 publicly available state-of-the-art methods when considering a wide range of patient groups. Thus, we present a new method for obtaining hippocampal volumes, an important biomarker in aging, disease, and dementia.

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