An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

Abstract

The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n=30), koilocytosis (n=46), CIN 1 (n=52), CIN 2 (n=56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology.

Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection

“…5-8, the first image of each row consists of the manual segmentation provided by our medical expert. 4 For each tissue image, the regions are labeled as either cancerous or normal and the boundary between these regions are drawn in red. In between these boundaries, there are also regions that can be included in either side without affecting the medical interpretation; such regions are shaded in red.…”

“…However, as it mainly relies on the visual interpretation, this examination may lead to a considerable amount of subjectivity, especially in cancer grading [1,2]. To reduce this subjectivity, it has been proposed to use computational methods that rely on the quantification of a tissue by defining mathematical features [3][4][5][6][7]. Although the very first step in this quantification is the segmentation of a tissue image into homogeneous regions, these studies have not mainly focused on this problem and have extracted features from the tissue image assuming that it is homogeneous.…”

Karyometry detects subvisual differences in chromatin organization state between cribriform and flat high-grade prostatic intraepithelial neoplasia

“…H&E-staining was used so that the results from image analysis could be directly compared and correlated with histopathologic assessment. Bahr 9 and Keenan et al 10 showed that data derived from H&E and Papanicolaou stains are linearly correlated with those from Feulgen.…”

Exploratory analysis of quantitative histopathology of cervical intraepithelial neoplasia: Objectivity, reproducibility, malignancy‐associated changes, and human papillomavirus

“…Other issues are more specific to the application, such as the sampling, the type of staining, and the normalization issues. Recently, Keenan et al (9) found that quantitative analysis of cervical biopsy specimens stained with hematoxylin and eosin (H&E) could provide discriminant power to separate the different grades of dysplasia. The question then arises regarding the need for using Feulgen-stained specimen, which requires additional cutting and staining steps.…”