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MAP-based Brain Tissue Segmentation using Manifold Learning and Hierarchical Max-Flow regularization

Martin Rajchl, John S.H. Baxter, A. Jonathan McLeod, Jing Yuan, Wu Qiu, Terry M. Peters, James A. White, Ali R. Khan
Imaging Laboratories, Robarts Research Institute, London, ON
Grand Challenge on MR Brain Image Segmentation workshop, 2014

@article{rajchl2014map,

   title={MAP-based Brain Tissue Segmentation using Manifold Learning and Hierarchical Max-Flow regularization},

   author={Rajchl, Martin and Baxter, John SH and McLeod, A Jonathan and Yuan, Jing and Qiu, Wu and Peters, Terry M and White, James A and Khan, Ali R},

   year={2014}

}

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We developed a fully-automatic multi-atlas initialized segmentation algorithm for tissue segmentation using multi-sequence MR images. The Generalized Hierarchical Max-Flow (HMF) [1] framework proposed in [2] is employed to regularize a maximum a-posteriori data term with a linear label-ordering constraint [3]. The data term is derived from two probabilistic cost functions, i) an intensity model from learned Gaussian Mixture Models (GMM) via Kohonnen Self-organizing maps (KSOM) and ii) a shape prior from multi-atlas labeling. These costs are combined and subsequently regularized using the GHMF framework. The algorithm is fully automated and major components of the image processing pipeline are implemented using General-Purpose Programming on Graphics Processing Units (GPGPU) to achieve a substantial increase in computation speed.
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