Combining a Patch-based Approach with a Non-rigid Registration-based Label Fusion Method for the Hippocampal Segmentation in Alzheimer’s Disease

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 15; no. 2; pp. 165 - 183
• Main Authors: Platero, Carlos, Tobar, M. Carmen
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-04-2017; Springer Nature B.V
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - pathology; Alzheimer's disease; Analysis of Variance; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Cognitive ability; Cognitive Dysfunction - pathology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer programs; Databases, Factual; Female; Freeware; Geriatrics; Hippocampus; Hippocampus - diagnostic imaging; Humans; Image processing; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Mental Status Schedule; Neurodegenerative diseases; Neurology; Neurosciences; NMR; Nuclear magnetic resonance; Older people; Open source software; Original Article; Pattern Recognition, Automated; Resonance; Segmentation; Similarity measures; Software; Hippocampal segmentation; Atlas-based segmentation; Image registration; Magnetic resonance imaging; Patch-based label fusion
• ISSN: 1539-2791; 1559-0089
• DOI: 10.1007/s12021-017-9323-3

We provide and evaluate an open-source software solution for automatically hippocampal segmentation from T1-weighted (T1w) magnetic resonance imaging (MRI). The method is applied for measuring the hippocampal volume, which allows discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC). The method is based on a fast patch-based label fusion method, whose selected patches and their weights are calculated from a combination of similarity measures between patches using intensity-based distances and labeling-based distances. These combined similarity measures produces better selection of the patches, and their weights are more robust. The algorithm is trained with the Harmonized Hippocampal Protocol (HarP). The proposal is compared with FreeSurfer and other label fusion methods. To evaluate the performance and the robustness of the proposed label fusion method, we employ two databases of T1w MRI of human brains. For AD vs NC, we obtain a high degree of accuracy, approximately 90 %. For MCI vs NC, we obtain accuracies around 75 %. The average time for the hippocampal segmentation from a T1w MRI is less than 17 minutes.