title: Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors.
abstract: We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, ours builds a set of candidate trees over many different subsets of points likely to belong to the optimal tree and then chooses the best one according to a global objective function that combines image evidence with geometric priors. Since the best tree does not necessarily span all the points, the algorithm is able to eliminate false detections while retaining the correct tree topology. Manually annotated brightfield micrographs, retinal scans and the DIADEM challenge datasets are used to evaluate the performance of our method. We used the DIADEM metric to quantitatively evaluate the topological accuracy of the reconstructions and showed that the use of the geometric regularization yields a substantial improvement.
authors.affiliation: Computer Vision Laboratory, Faculté Informatique et Communications, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. engin.turetken@epfl.ch
authors.forename: Engin
authors.initials: E
authors.lastname: Türetken
authors.forename: Germán
authors.initials: G
authors.lastname: González
authors.forename: Christian
authors.initials: C
authors.lastname: Blum
authors.forename: Pascal
authors.initials: P
authors.lastname: Fua
mesh_terms: D000465:Algorithms; D000818:Animals; D001369:Axons / Q000502:physiology*; D003712:Dendrites / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards / Q000639:trends; D021621:Imaging, Three-Dimensional / Q000379:methods* / Q000592:standards / Q000639:trends; D008959:Models, Neurological; D012984:Software / Q000592:standards* / Q000639:trends