title: Geometrical consistent 3D tracing of neuronal processes in ssTEM data.
abstract: In neuroanatomy, automatic geometry extraction of neurons from electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for tracing neuronal processes over serial sections for 3d reconstructions. The automatic processing pipeline combines the probabilistic output of a random forest classifier with geometrical consistency constraints which take the geometry of whole sections into account. Our experiments demonstrate significant improvement over grouping by Euclidean distance, reducing the split and merge error per object by a factor of two.
authors.affiliation: Department of Computer Science, ETH Zurich, Switzerland. verena.kaynig@inf.ethz.ch
authors.forename: Verena
authors.initials: V
authors.lastname: Kaynig
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Fuchs
authors.forename: Joachim M
authors.initials: JM
authors.lastname: Buhmann
mesh_terms: D000465:Algorithms; D001921:Brain / Q000648:ultrastructure*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D046529:Microscopy, Electron, Transmission / Q000379:methods*; D009474:Neurons / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity