Automating the Paris System for urine cytopathology—A hybrid deep‐learning and morphometric approach. Issue 2 (31st January 2019)

• Record Type: Journal Article
• Authors: Vaickus, Louis J.; Suriawinata, Arief A.; Wei, Jason W.; Liu, Xiaoying
• Is Part Of: Cancer cytopathology. Volume 127:Issue 2(2019)
• Page Start: 98
• Page End: 115
• Publication Date: 2019-01-31
• Subjects: automation -- deep learning -- Paris System -- urine cytopathology -- whole‐slide imaging (WSI); Cancer -- Cytopathology -- Periodicals; Pathology, Cellular -- Periodicals; Cytology -- Technique -- Periodicals; 611.01815
• DOI: 10.1002/cncy.22099
• Languages: English
• ISSNs: 1934-662X

Abstract

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. Methods: Whole‐slide images (WSI) of liquid‐based urine cytology specimens were extracted from 51 negative, 60 atypical, 52 suspicious, and 54 positive cases. Morphometric algorithms were applied to decompose images to their component parts; and statistics, including the NC ratio, were tabulated using segmentation algorithms to create organized data structures, dubbed rich information matrices (RIMs). These RIM objects were enhanced using deep‐learning algorithms to include qualitative measures. The augmented RIM objects were then used to reconstruct WSIs with filtering criteria and to generate pancellular statistical information. Results: The described system was used to calculate the N:C ratio for all cells, generate object classifications (atypical urothelial cell, squamous cell, crystal, etc), filter the original WSI to remove unwanted objects, rearrange the WSI to an efficient, condensed‐grid format, and generate pancellular statistics containing quantitative/qualitative data for every cell in a WSI. In addition to developing novel techniques for managing WSIs, a system capable of automatically tabulating the Paris System criteria also was generated. Conclusions: A hybrid deep‐learning and morphometric algorithm was developed for the analysis of urine cytology specimens that could reliably automate the Paris System and provide many avenues for increasing the efficiency of digital screening for urine WSIs and other cytology preparations. Abstract : The Paris System criteria are partially objective (in calculating the nuclear‐to‐cytoplasmic ratio) and partially subjective (in assessing nuclear atypia/irregularity/hyperchromasia), requiring a hybrid approach for automation. The authors report their development of a hybrid deep‐learning and morphometric algorithm for the analysis of urine cytology specimens that can automate the Paris System reliably and can provide many avenues for increasing the efficiency of digital screening for urine whole‐slide images and other cytology preparations.