ArticleNeuroinformatics2013
Automated segmentation of mouse brain images using multi-atlas multi-ROI deformation and label fusion.
Article in Neuroinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice.Bio-protocol · 2025Article
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- 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.Frontiers in aging neuroscience · 2022Article
- Label fusion method combining pixel greyscale probability for brain MR segmentation.Scientific reports · 2019Article
- Murine-specific Internal Dosimetry for Preclinical Investigations of Imaging and Therapeutic Agents.Health physics · 2018Article
- In vivo microscopic voxel-based morphometry with a brain template to characterize strain-specific structures in the mouse brain.Scientific reports · 2017Article
- Generation and Disease Model Relevance of a Manganese Enhanced Magnetic Resonance Imaging-Based NOD/scid-IL-2Rγc(null) Mouse Brain Atlas.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2016Article
- Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo cardiac phenotyping.Journal of medical imaging (Bellingham, Wash.) · 2015Article
- Multi-atlas segmentation of biomedical images: A survey.Medical image analysis · 2015Review
- Particle-guided image registration.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2013Article
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Authors and funding
2 authors.
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Abstract
We propose an automated multi-atlas and multi-ROI based segmentation method for both skull-stripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic resonance images (MRI). Three main steps are involved in our method. First, a region of interest (ROI) guided warping algorithm is designed to register multi-atlas images to the subject space, by considering more on the matching of image contents around the ROI boundaries which are more important for ROI labeling. Then, a multi-atlas and multi-ROI based deformable segmentation method is adopted to refine the ROI labeling result by deforming each ROI surface via boundary recognizers (i.e., SVM classifiers) trained on local surface patches. Finally, a local-mutual-information (MI) based multi-label fusion technique is proposed for allowing the atlases with better local image similarity with the subject to have more contributions in label fusion. The experimental results show that our method works better than the conventional methods on both in vitro and in vivo mouse brain datasets.
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Registered trials
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