title: Flash scanning electron microscopy.
abstract: Scanning Electron Microscopy (SEM) is an invaluable tool for biologists and neuroscientists to study brain structure at the intracellular level. While able to image tissue samples with up to 5 nm isotropic resolution, image acquisition is prohibitively slow and limits the size of processed samples. In this work, we propose a novel approach to speeding up imaging when looking for specific structures. Unlike earlier methods, we explicitly balance the conflicting requirements of spending enough time scanning potential regions of interest to ensure that all targets are found while not wasting time on unpromising regions. This is achieved by using a Markov Random Field to model target locations and optimizing scanning locations by using a Branch-and-Bound strategy. We show that our approach significantly outperforms state-of-the-art methods to locate mitochondria in brain tissue.
authors: Ecole Polytechnique Fédérale de Lausanne, Switzerland. raphael.sznitman@epfl.ch
authors: Raphael
authors: R
authors: Sznitman
authors: Ecole Polytechnique Fédérale de Lausanne, Switzerland.
authors: Aurelien
authors: A
authors: Lucchi
authors: Ecole Polytechnique Fédérale de Lausanne, Switzerland.
authors: Marco
authors: M
authors: Cantoni
authors: Ecole Polytechnique Fédérale de Lausanne, Switzerland.
authors: Graham
authors: G
authors: Knott
authors: Ecole Polytechnique Fédérale de Lausanne, Switzerland.
authors: Pascal
authors: P
authors: Fua
mesh_terms: D000465:Algorithms; D001921:Brain / Q000648:ultrastructure*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008855:Microscopy, Electron, Scanning / Q000379:methods*; D008928:Mitochondria / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity