Hippocampus MRI diagnosis based on deep learning in application of preliminary screening of Alzheimer’s disease

Abstract

The morphological changes of the hippocampus in brain Magnetic Resonance Imaging (MRI) images are of great significance for the early screening of Alzheimer's disease. Currently, in clinical practice, the diagnosis of the hippocampus is achieved manually by doctors with experience. Because the hippocampus has the characteristics of small size, complex shape, and indistinct boundary with surrounding structures, manual segmentation, and grading of the hippocampus in brain MRI is time-consuming and labor-intensive, which is susceptible to errors because of human subjective judgment. To address that, this paper proposes a hippocampal MRI diagnosis algorithm based on Faster R-CNN and Mask R-CNN. The main contributions are 1) automatic identification of hippocampus in brain MRI by Faster R-CNN neural network, 2) precisely segmenting the hippocampus and judging the atrophy level through Mask R-CNN. Case studies are performed on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and the medical records of the First Affiliated Hospital of Chongqing Medical University. Results indicate that the proposed method achieves a good segmentation effect on the hippocampus in the coronal MRI image of the brain and accurately grades the level of hippocampal atrophy, which can better assist doctors in diagnosing Alzheimer's disease.