title: New computerized color image analysis for the quantification of interstitial fibrosis in renal transplantation.
abstract: BACKGROUND
  Chronic allograft injury, the primary cause of late allograft failure in renal transplantation, can be diagnosed early at a preclinical stage by histopathological changes such as interstitial fibrosis (IF). Currently, assessed by semiquantitative analysis in the Banff classification, IF quantification is limited by pathologist's subjective interpretation.
  
  
  METHODS
  We have designed algorithms dedicated to quantify IF by computerized color image analysis. This innovative and objective software automatically extracts the green areas characteristic of IF in Masson's trichrome based on color image segmentation followed by removal of nonspecific IF staining (capsula, sclerosis glomeruli and normal glomeruli, normal basement membrane) and computes an index. It also counts automatically the number of glomeruli. Sixty-seven Masson stained renal transplant biopsies at various IF stages were imaged using a digital color camera mounted on a microscope. We tested the robustness of the method against varying acquisition parameters.
  
  
  RESULTS
  We demonstrated that the parameters do not have an impact on this quantification and that the algorithm is able to handle biopsy color variations. The intra- and interobserver reproducibility was good (P<0.003). The kappa coefficient that was performed on another set of 90 biopsies to evaluate the concordance of our method with an expert Banff quantification was 0.68, indicating a substantial agreement. Finally, the computerized IF correlated with renal function.
  
  
  CONCLUSIONS
  This study demonstrates that computerized color image analysis is a reliable and reproducible method to evaluate renal IF in routine practice and in multi-centric studies.
authors.affiliation: Institut Pasteur, Quantitative Image Analysis Unit, Cell Biology and Infection Department, CNRS URA 2582, Paris, France. vmeasyed@pasteur.fr
authors.forename: Vannary
authors.initials: V
authors.lastname: Meas-Yedid
authors.forename: Aude
authors.initials: A
authors.lastname: Servais
authors.forename: Laure-Hélène
authors.initials: LH
authors.lastname: Noël
authors.forename: Clarisse
authors.initials: C
authors.lastname: Panterne
authors.forename: Paul
authors.initials: P
authors.lastname: Landais
authors.forename: Nicolas
authors.initials: N
authors.lastname: Hervé
authors.forename: Nicole
authors.initials: N
authors.lastname: Brousse
authors.forename: Henri
authors.initials: H
authors.lastname: Kreis
authors.forename: Christophe
authors.initials: C
authors.lastname: Legendre
authors.forename: Eric
authors.initials: E
authors.lastname: Thervet
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Olivo-Marin
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Morelon
mesh_terms: D000465:Algorithms; D001706:Biopsy; D003116:Color; D005355:Fibrosis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000473:pathology* / Q000502:physiology; D016030:Kidney Transplantation / Q000473:pathology*; D015588:Observer Variation; D015203:Reproducibility of Results; D012984:Software; D014184:Transplantation, Homologous