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

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.