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

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. Copyright (C) 2000 John Wiley and Sons, Ltd.

The impact of virtual simulation in palliative radiotherapy for non-small-cell lung cancer.

Background and purpose: Radiotherapy is widely used to palliate local symptoms in non-small-cell lung cancer. Using conventional X-ray simulation, it is often difficult to accurately localize the extent of the tumour. We report a randomized, double blind trial comparing target localization with conventional and virtual simulation.Methods: Eighty-six patients underwent both conventional and virtual simulation. The conventional simulator films were compared with digitally reconstructed radiographs (DRRs) produced from the computed tomography (CT) data. The treatment fields defined by the clinicians using each modality were compared in terms of field area, position and the implications for target coverage.Results: Comparing fields defined by each study arm, there was a major mis-match in coverage between fields in 66.2% of cases, and a complete match in only 5.2% of cases. In 82.4% of cases, conventional simulator fields were larger (mean 24.5+/-5.1% (95% confidence interval)) than CT-localized fields, potentially contributing to a mean target under-coverage of 16.4+/-3.5% and normal tissue over-coverage of 25.4+/-4.2%.Conclusions: CT localization and virtual simulation allow more accurate definition of the target volume. This could enable a reduction in geographical misses, while also reducing treatment-related toxicity.