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 - Seo, Seung Yeon; Kim, Soo-Jong; Oh, Jungsu S.; Chung, Jinwha; Kim, Seog-Young; Oh, Seung Jun; Joo, Segyeong; Kim, Jae Seung (Frontiers in Aging Neuroscience, 2022)
Although skull-stripping and brain region segmentation are essential for precise quantitative analysis of positron emission tomography (PET) of mouse brains, deep learning-based unified solutions, particularly for spatial normalization, have posed a challenging problem in deep learning-based image processing. In this study, we propose an approach based on deep learning to resolve these issues. We generated both skull-stripping masks and individual-brain-specific volumes-of-interest (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 18F FDG PET scans twice, before and after the administration of human immunoglobin or antibody-based treatments. For training the CNN, manually traced brain masks and iSN-based target volume of interest (VOI) 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 t-tests of SUVR % changes in target VOIs before and after treatment. Our deep CNN-based method successfully generated brain parenchyma mask and target VOIs, showing no significant difference from conventional VOI methods in SUVR correlation analysis, thus establishing methods of template-based VOI without spatial normalization.