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Seo SY, Kim SJ, Oh JS, Chung J, Kim SY, Oh SJ, Joo S, Kim JS. 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. Front Aging Neurosci 2022; 14:807903. [PMID: 35309883 PMCID: PMC8931825 DOI: 10.3389/fnagi.2022.807903] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/17/2022] [Indexed: 02/03/2023] Open
Abstract
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 18F 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 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, which shows no significant difference from conventional VOI methods in SUVR correlation analysis, thus establishing methods of template-based VOI without SN.
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Affiliation(s)
- Seung Yeon Seo
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
| | - Soo-Jong Kim
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Songpa-gu, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon-si, South Korea
| | - Jungsu S. Oh
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
- *Correspondence: Jungsu S. Oh, ;
| | - Jinwha Chung
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
| | - Seog-Young Kim
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
| | - Seung Jun Oh
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
| | - Segyeong Joo
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
| | - Jae Seung Kim
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Songpa-gu, South Korea
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