Abstract：
: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 MRBrainS13 databases, 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.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Rajchl M,Baxter JS,McLeod AJ,Yuan J,Qiu W,Peters TM,Khan ARdoi
10.1016/j.media.2015.05.005subject
Has Abstractpub_date
2016-01-01 00:00:00pages
45-56eissn
1361-8415issn
1361-8423pii
S1361-8415(15)00072-9journal_volume
27pub_type
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