Philippe Belhomme

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.

A general framework for the segmentation of follicular lymphoma virtual slides

Follicular lymphoma (FL) is one of the most common types of non-Hodgkins lmphomas in the world. Diagnosis of FL is based on morphological and immunohistochemical characteristics found on tissue sections. Our projects 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.

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.

Building of a composite virtual slide from contiguous tissue samples

BackgroundCurrently 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 methodsImages 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.ResultsSixty 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.ConclusionsThe proposed method is efficient and able to adapt itself to daily work conditions of classical pathology laboratories.

Breakthrough Technologies in Digital Pathology

The 12th European Congress on Digital Pathology (ECDP 2014) was held from 18 to 21 June 2014 at the College des Bernardins in Paris, thanks to the support of the French Pathology Society, and with the collaboration of the Association for Developing Informatics in Cytology and Anatomic Pathology, ADICAP and the French Cellular Haematology Group, GFHC. By bringing along pathologists, scientists and industrials, this conference highlighted the dynamics of the communities involved in the evolution towards digital pathology. Among the challenges raised by this evolution, being able to bring justified and traceable responses has become an ethical priority for the patients and the healthcare professionals. From this perspective, the digital pathology will certainly bring an important increase in the quality of healthcare. In order to assess this evolution, seven journal papers have been selected from the ECDP 2014 presentations, for their pertinence and their originality, from the information and imaging technologies perspective. We entitled this special issue “Breakthrough Technologies in Digital Pathology”, as a stimulus to new challenges for the future of digital pathology. New perspectives about the use of semantics at the helm for a knowledgeable Whole Slide Image (WSI) exploration are presented. Indeed, in the perspective of a traceable ethical healthcare as the rise of big data challenges, semantic technologies will play a fundamental role in the future of digital as the integrative pathology. The modelling of visual appearance, very close to the cognitive and perceptual points of view, is considered as a key point for an ergonomic interface between WSI and the pathologist. In the continuation of the perceptive problems, the modelling of the visual appearance as a comparative study between frequential and spatial colour textons is presented followed by an interesting approach using Fourier ptychography. Coming close to computer-aided diagnosis, we selected an interesting weak supervision approach. Finally, promising advances in heterogeneity and precise localisation problems complete the range of the selected papers, coming closer and closer to daily routine in histopathology. We believe that the digital pathology will be a bridge for a smoother integration of all these challenges and viewpoints in the future of the pathology and we hope being able to participate to the on-going revolution of digital pathology by bringing them to your attention.

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 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.