Automating the Paris System for urine cytopathology-A hybrid deep-learning and morphometric approach.

Journal: Cancer cytopathology
Published Date:

Abstract

BACKGROUND: The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid-based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear-to-cytoplasmic (N:C) ratio and produce a qualitative "atypia score." The authors propose a hybrid deep-learning and morphometric model that reliably automates the Paris System.

Authors

  • Louis J Vaickus
    Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
  • Arief A Suriawinata
    Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
  • Jason W Wei
    Department of Computer Science, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
  • Xiaoying Liu
    Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.