title: Objective detection of apoptosis in rat renal tissue sections using light microscopy and free image analysis software with subsequent machine learning: Detection of apoptosis in renal tissue.
abstract: OBJECTIVE
  The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation.
  
  
  METHODS
  A total of 17 images of kidney histological slide samples from male Wistar rats were used. The slides were photographed using an Axio Zeiss Vert.A1 microscope with a 40x objective lens coupled with an Axio Cam MRC Zeiss camera and Zen 2012 software. The images were analyzed using CellProfiler (version 2.1.1) and CellProfiler Analyst open-source software.
  
  
  RESULTS
  Out of the 10,378 objects, 4970 (47,9%) were identified as TUNEL positive, and 5408 (52,1%) were identified as TUNEL negative. On average, the sensitivity and specificity values of the machine learning approach were 0.80 and 0.77, respectively.
  
  
  CONCLUSIONS
  Image cytometry provides a quantitative analytical alternative to the more traditional qualitative methods more commonly used in studies.
authors.affiliation: Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil.
authors.forename: Nayana Damiani
authors.initials: ND
authors.lastname: Macedo
authors.affiliation: Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil.
authors.forename: Aline Rodrigues
authors.initials: AR
authors.lastname: Buzin
authors.affiliation: Department of Morphology, Federal University of Espírito Santo, Vitória, ES, Brazil; Faculty of Medicine Carl Gustav Curav-Technical University Dresden, Dresden, Germany.
authors.forename: Isabela Bastos Binotti Abreu
authors.initials: IB
authors.lastname: de Araujo
authors.affiliation: Department of Morphology, Federal University of Espírito Santo, Vitória, ES, Brazil.
authors.forename: Breno Valentim
authors.initials: BV
authors.lastname: Nogueira
authors.affiliation: Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil.
authors.forename: Tadeu Uggere
authors.initials: TU
authors.lastname: de Andrade
authors.affiliation: Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil.
authors.forename: Denise Coutinho
authors.initials: DC
authors.lastname: Endringer
authors.affiliation: Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil. Electronic address: dominik.lenz@gmail.com.
authors.forename: Dominik
authors.initials: D
authors.lastname: Lenz
mesh_terms: D000818:Animals; D017209:Apoptosis; D007091:Image Processing, Computer-Assisted; D007668:Kidney / Q000648:ultrastructure*; D000069550:Machine Learning; D008853:Microscopy; D051381:Rats; D012984:Software
keywords: Automated detection; Image analysis; Machine learning; Tissue analysis; Tissue cytometry