期刊
MEDICAL IMAGE ANALYSIS
卷 27, 期 -, 页码 45-56出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2015.05.005
关键词
ASETS; Multi-region segmentation; Convex optimization; Kohonen self-organizing map; GPGPU
类别
资金
- Canada Institutes of Health Research [MOP 89844]
- Ontario Graduate Scholarship
- Vanier Canada Graduate Scholarship
- Canada Institutes of Health Research Fellowship
- [P50 AG05681]
- [P01 AG03991]
- [R01 AG021910]
- [P50 MH071616]
- [U24 RR021382]
- [R01 MH56584]
The incorporation of intensity, spatial, and topological information into large-scale multi-region segmentation has been a topic of ongoing research in medical image analysis. Multi-region segmentation problems, such as segmentation of brain structures, pose unique challenges in image segmentation in which regions may not have a defined intensity, spatial, or topological distinction, but rely on a combination of the three. We propose a novel framework within the Advanced segmentation tools (ASETS)(2), which combines large-scale Gaussian mixture models trained via Kohonen self-organizing maps, with deformable registration, and a convex max-flow optimization algorithm incorporating region topology as a hierarchy or tree. Our framework is validated on two publicly available neuroimaging datasets, the OASIS and MRBrainSlIdatabases, against the more conventional Potts model, achieving more accurate segmentations. Each component is accelerated using general-purpose programming on graphics processing Units to ensure computational feasibility. (C) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据