Unified Deep Learning-Based Mouse Brain MR Segmentation: Template-Based Individual Brain Positron Emission Tomography Volumes-of-Interest Generation Without Spatial Normalization in Mouse Alzheimer Model

• Published in: Frontiers in aging neuroscience 2022-03, Vol.14, p.807903-807903
• Main Authors: Seo, Seung Yeon, Kim, Soo-Jong, Oh, Jungsu S, Chung, Jinwha, Kim, Seog-Young, Oh, Seung Jun, Joo, Segyeong, Kim, Jae Seung
• Format: Article
• Language: English
• Subjects: Aging; Alzheimer's disease; Amyloid precursor protein; Animal models; Brain; Cerebellum; Correlation analysis; deep convolutional-neural-network (CNN); Deep learning; Image processing; inverse-spatial-normalization (iSN); Magnetic resonance imaging; Medical imaging; mouse brain; Neostriatum; Neural networks; Neurodegenerative diseases; Neuroscience; Parenchyma; Positron emission tomography; Presenilin 1; Registration; Segmentation; Skull; skull-stripping; Software; template-based volume of interest (VOI); Thalamus; Tomography
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2022.807903

Although skull-stripping and brain region segmentation are essential for precise quantitative analysis of positron emission tomography (PET) of mouse brains, deep learning (DL)-based unified solutions, particularly for spatial normalization (SN), have posed a challenging problem in DL-based image processing. In this study, we propose an approach based on DL to resolve these issues. We generated both skull-stripping masks and individual brain-specific volumes-of-interest (VOIs-cortex, hippocampus, striatum, thalamus, and cerebellum) based on inverse spatial normalization (iSN) and deep convolutional neural network (deep CNN) models. We applied the proposed methods to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and F FDG PET scans two times, before and after the administration of human immunoglobulin or antibody-based treatments. For training the CNN, manually traced brain masks and iSN-based target VOIs were used as the label. We compared our CNN-based VOIs with conventional (template-based) VOIs in terms of the correlation of standardized uptake value ratio (SUVR) by both methods and two-sample -tests of SUVR % changes in target VOIs before and after treatment. Our deep CNN-based method successfully generated brain parenchyma mask and target VOIs, which shows no significant difference from conventional VOI methods in SUVR correlation analysis, thus establishing methods of template-based VOI without SN.