title: Detection of spatially correlated objects in 3D images using appearance models and coupled active contours.
abstract: We consider the problem of segmenting 3D images that contain a dense collection of spatially correlated objects, such as fluorescent labeled cells in tissue. Our approach involves an initial modeling phase followed by a data-fitting segmentation phase. In the first phase, cell shape (membrane bound) is modeled implicitly using a parametric distribution of correlation function estimates. The nucleus is modeled for its shape as well as image intensity distribution inspired from the physics of its image formation. In the second phase, we solve the segmentation problem using a variational level-set strategy with coupled active contours to minimize a novel energy functional. We demonstrate the utility of our approach on multispectral fluorescence microscopy images.
authors.affiliation: Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. kishore@hms.harvard.edu
authors.forename: Kishore
authors.initials: K
authors.lastname: Mosaliganti
authors.forename: Arnaud
authors.initials: A
authors.lastname: Gelas
authors.forename: Alexandre
authors.initials: A
authors.lastname: Gouaillard
authors.forename: Ramil
authors.initials: R
authors.lastname: Noche
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Obholzer
authors.forename: Sean
authors.initials: S
authors.lastname: Megason
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus / Q000648:ultrastructure*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity