title: Candidate sampling for neuron reconstruction from anisotropic electron microscopy volumes.
abstract: The automatic reconstruction of neurons from stacks of electron microscopy sections is an important computer vision problem in neuroscience. Recent advances are based on a two step approach: First, a set of possible 2D neuron candidates is generated for each section independently based on membrane predictions of a local classifier. Second, the candidates of all sections of the stack are fed to a neuron tracker that selects and connects them in 3D to yield a reconstruction. The accuracy of the result is currently limited by the quality of the generated candidates. In this paper, we propose to replace the heuristic set of candidates used in previous methods with samples drawn from a conditional random field (CRF) that is trained to label sections of neural tissue. We show on a stack of Drosophila melanogaster neural tissue that neuron candidates generated with our method produce 30% less reconstruction errors than current candidate generation methods. Two properties of our CRF are crucial for the accuracy and applicability of our method: (1) The CRF models the orientation of membranes to produce more plausible neuron candidates. (2) The interactions in the CRF are restricted to form a bipartite graph, which allows a great sampling speed-up without loss of accuracy.
authors.forename: Jan
authors.initials: J
authors.lastname: Funke
authors.forename: Julien N P
authors.initials: JN
authors.lastname: Martel
authors.forename: Stephan
authors.initials: S
authors.lastname: Gerhard
authors.forename: Bjoern
authors.initials: B
authors.lastname: Andres
authors.forename: Dan C
authors.initials: DC
authors.lastname: Cireşan
authors.forename: Alessandro
authors.initials: A
authors.lastname: Giusti
authors.forename: Luca M
authors.initials: LM
authors.lastname: Gambardella
authors.forename: Jürgen
authors.initials: J
authors.lastname: Schmidhuber
authors.forename: Hanspeter
authors.initials: H
authors.lastname: Pfister
authors.forename: Albert
authors.initials: A
authors.lastname: Cardona
authors.forename: Matthew
authors.initials: M
authors.lastname: Cook
mesh_terms: D000465:Algorithms; D000818:Animals; D016880:Anisotropy; D002478:Cells, Cultured; D003627:Data Interpretation, Statistical; D004331:Drosophila melanogaster; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008854:Microscopy, Electron / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique