title: Toward digital staining using imaging mass spectrometry and random forests.
abstract: We show on imaging mass spectrometry (IMS) data that the Random Forest classifier can be used for automated tissue classification and that it results in predictions with high sensitivities and positive predictive values, even when intersample variability is present in the data. We further demonstrate how Markov Random Fields and vector-valued median filtering can be applied to reduce noise effects to further improve the classification results in a posthoc smoothing step. Our study gives clear evidence that digital staining by means of IMS constitutes a promising complement to chemical staining techniques.
authors.affiliation: Heidelberg Collaboratory for Image Processing (HCI), Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Speyerer Strasse 6, 69115 Heidelberg, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Hanselmann
authors.forename: Ullrich
authors.initials: U
authors.lastname: Köthe
authors.forename: Marc
authors.initials: M
authors.lastname: Kirchner
authors.forename: Bernhard Y
authors.initials: BY
authors.lastname: Renard
authors.forename: Erika R
authors.initials: ER
authors.lastname: Amstalden
authors.forename: Kristine
authors.initials: K
authors.lastname: Glunde
authors.forename: Ron M A
authors.initials: RM
authors.lastname: Heeren
authors.forename: Fred A
authors.initials: FA
authors.lastname: Hamprecht
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008390:Markov Chains; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D009369:Neoplasms / Q000473:pathology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated; D040901:Proteomics / Q000379:methods*; D012984:Software