title: Exploiting enclosing membranes and contextual cues for mitochondria segmentation.
abstract: In this paper, we improve upon earlier approaches to segmenting mitochondria in Electron Microscopy images by explicitly modeling the double membrane that encloses mitochondria, as well as using features that capture context over an extended neighborhood. We demonstrate that this results in both improved classification accuracy and reduced computational requirements for training.
authors.forename: Aurélien
authors.initials: A
authors.lastname: Lucchi
authors.forename: Carlos
authors.initials: C
authors.lastname: Becker
authors.forename: Pablo Márquez
authors.initials: PM
authors.lastname: Neila
authors.forename: Pascal
authors.initials: P
authors.lastname: Fua
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000648:ultrastructure*; D002478:Cells, Cultured; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008854:Microscopy, Electron / Q000379:methods*; D008928:Mitochondria / Q000648:ultrastructure*; D051336:Mitochondrial Membranes / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity