Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
Cancer Cytopathol. 2019 Apr;127(4):218-221. doi: 10.1002/cncy.22116.
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.
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.
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.
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.
巴黎尿液细胞学系统（巴黎系统）成功地使基于液体的尿液标本分析更具可重复性。任何试图使该系统自动化的算法都必须准确估计核质比并产生定性的“非典型评分”。作者提出了一种混合深度学习和形态计量学模型，可以可靠地自动化巴黎系统。
从 51 例阴性、60 例非典型、52 例可疑和 54 例阳性的基于液体的尿液细胞学标本中提取全玻片图像（WSI）。形态计量学算法被应用于将图像分解为其组成部分；使用分割算法对包括核质比在内的统计数据进行制表，以创建有组织的数据结构，称为丰富信息矩阵（RIM）。使用深度学习算法增强这些 RIM 对象，以包括定性测量。然后使用增强的 RIM 对象对 WSI 进行过滤标准重建，并生成全细胞统计信息。
描述的系统用于计算所有细胞的核质比，生成对象分类（非典型尿路上皮细胞、鳞状细胞、晶体等），过滤原始 WSI 以去除不需要的对象，重新排列 WSI 以有效、浓缩的网格格式，并生成包含每个细胞的定量/定性数据的全细胞统计信息。除了开发用于管理 WSI 的新技术外，还生成了一个能够自动制表巴黎系统标准的系统。
开发了一种用于尿液细胞学标本分析的混合深度学习和形态计量学算法，可以可靠地自动化巴黎系统，并为提高尿液 WSI 和其他细胞学标本的数字筛查效率提供多种途径。
