title: 3D-SIFT-Flow for atlas-based CT liver image segmentation.
abstract: OBJECTIVE
  In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images.
  
  
  METHODS
  In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases.
  
  
  RESULTS
  Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%.
  
  
  CONCLUSIONS
  Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.
authors.affiliation: State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191, China and Research Institute of Beihang University in Shenzhen and Microsoft Research, Beijing 100080, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.
authors.forename: Chenchao
authors.initials: C
authors.lastname: Xu
authors.affiliation: School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.
authors.forename: Xiao
authors.initials: X
authors.lastname: Kuang
authors.affiliation: Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.
authors.forename: Hongkai
authors.initials: H
authors.lastname: Wang
authors.affiliation: Microsoft Research, Beijing 100080, China.
authors.forename: Eric I-Chao
authors.initials: EI
authors.lastname: Chang
authors.affiliation: Institute for Infocomm Research (I2R), Singapore 138632.
authors.forename: Weimin
authors.initials: W
authors.lastname: Huang
authors.affiliation: Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191, China.
authors.forename: Yubo
authors.initials: Y
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D001271:Atlases as Topic; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*