title: Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images.
abstract: BACKGROUND
  Recently, confocal light sheet microscopy has enabled high-throughput acquisition of whole mouse brain 3D images at the micron scale resolution. This poses the unprecedented challenge of creating accurate digital maps of the whole set of cells in a brain.
  
  
  RESULTS
  We introduce a fast and scalable algorithm for fully automated cell identification. We obtained the whole digital map of Purkinje cells in mouse cerebellum consisting of a set of 3D cell center coordinates. The method is accurate and we estimated an F1 measure of 0.96 using 56 representative volumes, totaling 1.09 GVoxel and containing 4138 manually annotated soma centers.
  
  
  METHODS
  Source code and its documentation are available at http://bcfind.dinfo.unifi.it/. The whole pipeline of methods is implemented in Python and makes use of Pylearn2 and modified parts of Scikit-learn. Brain images are available on request.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Information Engineering (DINFO), Università di Firenze, 50139 Firenze, Italy, European Laboratory for Nonlinear Spectroscopy (LENS), Università di Firenze, 50019 Sesto Fiorentino, Italy and Integrated Research Centre, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Frasconi
authors.affiliation: Department of Information Engineering (DINFO), Università di Firenze, 50139 Firenze, Italy, European Laboratory for Nonlinear Spectroscopy (LENS), Università di Firenze, 50019 Sesto Fiorentino, Italy and Integrated Research Centre, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
authors.forename: Ludovico
authors.initials: L
authors.lastname: Silvestri
authors.affiliation: Department of Information Engineering (DINFO), Università di Firenze, 50139 Firenze, Italy, European Laboratory for Nonlinear Spectroscopy (LENS), Università di Firenze, 50019 Sesto Fiorentino, Italy and Integrated Research Centre, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Soda
authors.affiliation: Department of Information Engineering (DINFO), Università di Firenze, 50139 Firenze, Italy, European Laboratory for Nonlinear Spectroscopy (LENS), Università di Firenze, 50019 Sesto Fiorentino, Italy and Integrated Research Centre, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Cortini
authors.affiliation: Department of Information Engineering (DINFO), Università di Firenze, 50139 Firenze, Italy, European Laboratory for Nonlinear Spectroscopy (LENS), Università di Firenze, 50019 Sesto Fiorentino, Italy and Integrated Research Centre, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
authors.forename: Francesco S
authors.initials: FS
authors.lastname: Pavone
authors.affiliation: Department of Information Engineering (DINFO), Università di Firenze, 50139 Firenze, Italy, European Laboratory for Nonlinear Spectroscopy (LENS), Università di Firenze, 50019 Sesto Fiorentino, Italy and Integrated Research Centre, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
authors.forename: Giulio
authors.initials: G
authors.lastname: Iannello
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D051379:Mice; D018613:Microscopy, Confocal / Q000379:methods*; D009474:Neurons / Q000166:cytology*