title: Quantitative analysis of high-resolution microendoscopic images for diagnosis of neoplasia in patients with Barrett's esophagus.
abstract: OBJECTIVE
  Previous studies show that microendoscopic images can be interpreted visually to identify the presence of neoplasia in patients with Barrett's esophagus (BE), but this approach is subjective and requires clinical expertise. This study describes an approach for quantitative image analysis of microendoscopic images to identify neoplastic lesions in patients with BE.
  
  
  METHODS
  Images were acquired from 230 sites from 58 patients by using a fiberoptic high-resolution microendoscope during standard endoscopic procedures. Images were analyzed by a fully automated image processing algorithm, which automatically selected a region of interest and calculated quantitative image features. Image features were used to develop an algorithm to identify the presence of neoplasia; results were compared with a histopathology diagnosis.
  
  
  RESULTS
  A sequential classification algorithm that used image features related to glandular and cellular morphology resulted in a sensitivity of 84% and a specificity of 85%. Applying the algorithm to an independent validation set resulted in a sensitivity of 88% and a specificity of 85%.
  
  
  CONCLUSIONS
  This pilot study demonstrates that automated analysis of microendoscopic images can provide an objective, quantitative framework to assist clinicians in evaluating esophageal lesions from patients with BE. (
  
  
  BACKGROUND
  NCT01384227 and NCT02018367.).
authors.affiliation: Department of Bioengineering, Rice University, Houston, Texas, USA; Department of Neurosurgery, The University of Texas Medical School at Houston, Houston, Texas, USA.
authors.forename: Dongsuk
authors.initials: D
authors.lastname: Shin
authors.affiliation: Division of Gastroenterology, The Mount Sinai Medical Center, New York, New York, USA.
authors.forename: Michelle H
authors.initials: MH
authors.lastname: Lee
authors.affiliation: Department of Pathology, The Mount Sinai Medical Center, New York, New York, USA.
authors.forename: Alexandros D
authors.initials: AD
authors.lastname: Polydorides
authors.affiliation: Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.
authors.forename: Mark C
authors.initials: MC
authors.lastname: Pierce
authors.affiliation: Division of Gastroenterology, The Mount Sinai Medical Center, New York, New York, USA; Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
authors.forename: Peter M
authors.initials: PM
authors.lastname: Vila
authors.affiliation: Division of Gastroenterology, The Mount Sinai Medical Center, New York, New York, USA; Division of Digestive Diseases, Yale-New Haven Hospital, New Haven, Connecticut, USA.
authors.forename: Neil D
authors.initials: ND
authors.lastname: Parikh
authors.affiliation: Department of Pathology, Baylor College of Medicine, Houston, Texas, USA.
authors.forename: Daniel G
authors.initials: DG
authors.lastname: Rosen
authors.affiliation: Division of Gastroenterology, The Mount Sinai Medical Center, New York, New York, USA.
authors.forename: Sharmila
authors.initials: S
authors.lastname: Anandasabapathy
authors.affiliation: Department of Bioengineering, Rice University, Houston, Texas, USA.
authors.forename: Rebecca R
authors.initials: RR
authors.lastname: Richards-Kortum
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis / Q000473:pathology*; D000465:Algorithms; D001471:Barrett Esophagus / Q000175:diagnosis / Q000473:pathology*; D004938:Esophageal Neoplasms / Q000175:diagnosis / Q000473:pathology*; D004945:Esophagoscopy; D004947:Esophagus / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069416:Intravital Microscopy; D010865:Pilot Projects; D012680:Sensitivity and Specificity