Building of a composite virtual slide from contiguous tissue samples

Background

Currently available microscope slide scanners produce whole slide images at various resolutions from histological sections. Nevertheless, acquisition area and so visualization of large tissue samples are limited by the standardized size of glass slides, used daily in pathology departments. The proposed solution has been developed to build composite virtual slides from images of large tumor fragments.

Materials and methods

Images of HES or immunostained histological sections of carefully labeled fragments from a representative slice of breast carcinoma were acquired with a digital slide scanner at a magnification of 20×. The tiling program involves three steps: the straightening of tissue fragment images using polynomial interpolation method, and the building and assembling of strips of contiguous tissue sample whole slide images in × and y directions. The final image is saved in a pyramidal BigTiff file format. The program has been tested on several tumor slices. A correlation quality control has been done on five images artificially cut.

Results

Sixty tumor slices from twenty surgical specimens, cut into two to twenty six pieces, were reconstructed. A median of 98.71% is obtained by computing the correlation coefficients between native and reconstructed images for quality control.

Conclusions

The proposed method is efficient and able to adapt itself to daily work conditions of classical pathology laboratories.

Heterogeneity assessment of histological tissue sections in whole slide images

Computerized image analysis (IA) can provide quantitative and repeatable object measurements by means of methods such as segmentation, indexation, classification, etc. Embedded in reliable automated systems, IA could help pathologists in their daily work and thus contribute to more accurate determination of prognostic histological factors on whole slide images. One of the key concept pathologists want to dispose of now is a numerical estimation of heterogeneity. In this study, the objective is to propose a general framework based on the diffusion maps technique for measuring tissue heterogeneity in whole slide images and to apply this methodology on breast cancer histopathology digital images.

Abstract B116: A methodology to ensure and improve accuracy of Ki67 digital immunohistochemistry analysis in breast cancer tissue

Background: Immunohistochemical Ki67 evaluation reflects proliferative activity and is one of most important prognostic/predictive markers of breast cancer. However, standardized and efficient methodologies to accurately and reproducibly measure the Ki67 expression are still to come. Besides tissue processing, sampling, intra-tumour variability, and many other aspects to be considered, key element of the methodology remains accurate enumeration of Ki67-labelling index (LI). We aimed to develop a methodology to estimate and improve accuracy of automated image analysis (IA) approach.

Methods: Tissue microarrays (1 mm diameter spot per patient, n=164) from invasive ductal breast carcinoma, stained for Ki67 and digitized by Aperio XT scanner, were used for the study. Reference values (RV) were obtained by counting the LI using stereological frame overlaid on a spot image. To test the degree of inter-observer variation in establishing the RV, the frame counts were performed by 3 observers independently in a subset (n=30) of the TMA images. IA was performed with Aperio Genie/Nuclear algorithms enabling automated selection of tumour tissue. Accuracy of the IA compared to the RV was estimated based on ANOVA, correlation and regression analyses performed with SAS 9.3. Agreement between individual measurements was also estimated based on 95% confidence intervals calculated from the RV according to stereology rules. Several iterations of the IA with adjusted algorithm settings were performed to improve the accuracy. Highly automated calibration cycles were enabled by developing software to integrate processes of the image and statistical analyses. Visual evaluation for the LI on the same images was performed by 3 pathologists (P1, P2, P3).

Results: Inter-observer variation between 3 independent frame counts (n=30) was negligible by ANOVA (respectively, mean RV were 28.5, 28.6 and 29.9%) with correlation coefficients 0.97 and above. RV correlated strongly with IA (r=0.95) and P1, P2, P3 (r=0.86, r=0.90, r=0.92, respectively), p<0.0001. ANOVA revealed no significant pairwise differences of the LI means of RV(40%) versus IA(37%), P2(43%), or P3(44%); however, the IA versus P2, P3 differed, and P1(24%) was significantly lower compared to all other measurements (p<0.05). Regression analysis to predict the RV revealed best performance for the IA results with R-square of 0.90 compared to 0.74, 0.82, and 0.85 of P1, P2, and P3, respectively. The IA results reported above were achieved by a third iteration of the IA calibration, while the results of the initial two IA settings revealed a significant bias to lower values by ANOVA and lower R-square values (0.86 and 0.87) by regression analyses, when compared to the RV. Similarly, correlation coefficients and agreement between individual IA measurements and RV improved during the calibration process.

Conclusion: Our experiments provide sound and efficient methodology to achieve accurate immunohistochemical Ki67 enumeration by IA, enabled by proper validation and calibration of the measurement against RV obtained by stereological frame counts.

Citation Format: Arvydas Laurinavicius, Benoit Plancoulaine, Aida Laurinaviciene, Paulette Herlin, Raimundas Meskauskas, Indra Baltrusaityte, Justinas Besusparis, Nicolas Elie, Philippe Belhomme, Yasir Iqbal, Catherine Bor-Angelier. A methodology to ensure and improve accuracy of Ki67 digital immunohistochemistry analysis in breast cancer tissue. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr B116.

A general framework for the segmentation of follicular lymphoma virtual slides

Follicular lymphoma (FL) is one of the most common types of non-Hodgkin's lmphomas in the world. Diagnosis of FL is based on morphological and immunohistochemical characteristics found on tissue sections. Our project's aim is to develop computer-aided analysis tools on virtual slide images (VSI) of lymphoid tissues with the purpose of improving the FL grading performed in malignant follicles. In this paper, we focus on the first step of our work, an automated system for detecting follicles in VSI of lymphoid tissues. To mimic the human expert process, the system works on low-resolution CD20 images and maps the follicle boundaries on high-resolution H&E images.

Towards a computer aided diagnosis system dedicated to virtual microscopy based on stereology sampling and diffusion maps

An original strategy is presented, combining stereological sampling methods based on test grids and data reduction methods based on diffusion maps, in order to build a knowledge image database with no bias introduced by a subjective choice of exploration areas. The practical application of the exposed methodology concerns virtual slides of breast tumors.

Classification of low resolution virtual slides from breast tumor sections: comparison between global and local analysis

In this paper, a comparison is made between a global and a local approach for computer-aided classification of virtual slides from breast tumor sections. The first approach classifies the images according to both color and texture information from the whole tissue, whereas the second one classifies them only on shape and texture from epithelial compartment. The originality of this study is that only low resolution virtual slides are used. As the cytological details are not visible at low resolution, the classification is only based on architectural aspect of each lesion. Experimental results on images from breast tumor sections show that some information is already present in low resolution virtual slides, allowing the classification in benign lesions versus malignant carcinomas.

Automated region of interest retrieval and classification using spectral analysis

Efficient use of whole slide imaging in pathology needs automated region of interest (ROI) retrieval and classification, through the use of image analysis and data sorting tools. One possible method for data sorting uses Spectral Analysis for Dimensionality Reduction. We present some interesting results in the field of histopathology and cytohematology.

In histopathology, we developed a Computer-Aided Diagnosis system applied to low-resolution images representing the totality of histological breast tumour sections. The images can be digitized directly at low resolution or be obtained from sub-sampled high-resolution virtual slides. Spectral Analysis is used (1) for image segmentation (stroma, tumour epithelium), by determining a «distance» between all the images of the database, (2) for choosing representative images and characteristic patterns of each histological type in order to index them, and (3) for visualizing images or features similar to a sample provided by the pathologist.

In cytohematology, we studied a blood smear virtual slide acquired through high resolution oil scanning and Spectral Analysis is used to sort selected nucleated blood cell classes so that the pathologist may easily focus on specific classes whose morphology could then be studied more carefully or which can be analyzed through complementary instruments, like Multispectral Imaging or Raman MicroSpectroscopy.

Image analysis software for automatic DNA ploidy assessment of archival solid tumours

BACKGROUND

Image cytometry has proved to provide a good alternative to flow cytometry for DNA ploidy measurement of archival tumors. However, when interactively done this technique is unable to give statistically valuable results within an acceptable time for clinical oncology.

METHODS

An image cytometer was developed for fully automatic DNA ploidy quantitation, focusing efforts on speed and accuracy. Software functionalities include systematic acquisition of fields on a microscopic slide, detection, localization and sorting of nuclei, computation of the DNA content together with post-processing tools, for a deeper analysis of the DNA ploidy diagram.

RESULTS

DNA ploidy analysis of archival breast carcinoma samples illustrates the accuracy of DNA ploidy measurements and the sensitivity in the detection of DNA ploidy abnormalities as a result of cell sorting.

CONCLUSIONS

Fully automatic image cytometry is able to combine qualities of flow cytometry (automatic analysis of a statistically significant collection of cell nuclei) with additional advantages: sorting of unwanted events (debris, stromal and inflammatory cell nuclei) and facilities for an a posteriori control of the quality of cell selection. This method is well suited to DNA ploidy analysis of archival cancer samples.

[Automation of the cytometric analysis of the DNA content of solid tumors]

An automatic machine, dedicated to solid tumor DNA ploidy quantitation has been built in order to provide pathologists with a tool usable in clinical practice. Main efforts were focused on an automation of each step of the analysis and on an elimination of any subjective choice, while preserving the quality of measurement. As the software is independent of the machine architecture, it offers performances which increase in parallel with the rapid evolution of the computers. An illustration of the various functionalities of the automaton is proposed through the study of deparaffined breast cancer samples.

Generalized region growing operator with optimal scanning: application to segmentation of breast cancer images

Segmentation of medical images is a complex problem owing to the large variety of their characteristics. In the automated analysis of breast cancers, two image classes may be distinguished according to whether one considers the quantification of DNA (grey level images of isolated nuclei) or the detection of immunohistochemical staining (colour images of histological sections). The study of these image classes generally involves the use of largely different image processing techniques. We therefore propose a new algorithm derived from the watershed transformation enabling us to solve these two segmentation problems with the same general approach. We then present visual and quantitative results to validate our method.