title: A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans.
abstract: An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results.
authors.affiliation: Department of Computer Sciences, ETSIDI, Technical University of Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.
authors.forename: Carlos
authors.identifier: 0000-0003-3712-8297
authors.initials: C
authors.lastname: Platero
authors.affiliation: Department of Computer Sciences, ETSIDI, Technical University of Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.
authors.forename: M Carmen
authors.identifier: 0000-0002-7370-6835
authors.initials: MC
authors.lastname: Tobar
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008099:Liver / Q000000981:diagnostic imaging* / Q000473:pathology; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011336:Probability; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*