Segmentation
Simultaneous multiple organs segmentation
Simultaneous multiple organs segmentation
Abstract
Abstract
We aim to segment a 3D CT volume data into a set of organs and solve the segmentation problem as a labeling problem, in which each label represents an organ. We build a higher-order Markov random field (MRF) an solve the energy minimization problem by graph cuts.
Publication
Publication
- Asuka Okagawa, Yuji Oyamada, Yoshihiko Mochizuki, and Hiroshi Ishikawa, "Multi-Organ Segmentation by Minimization of Higher-Order Energy for CT Boundary," IAPR International Conference on Machine Vision Applications (MVA) 2015, [pdf, bib]
- Minato Morita, Asuka Okagawa, Yuji Oyamada, Yoshihiko Mochizuki, and Hiroshi Ishikawa, "Multiple-Organ Segmentation Based on Spatially-Divided Neighboring Data Energy ," IAPR International Conference on Machine Vision Applications (MVA) 2015, [pdf, bib]