A Framework for 3D Vessel Analysis using Whole Slide Images of Liver Tissue Sections

An integrative web-based software tool for multi-dimensional pathology whole-slide image analytics

Objective.In the era of precision medicine, human tumor atlas-oriented studies have been significantly facilitated by high-resolution, multi-modal tissue based microscopic pathology image analytics. To better support such tissue-based investigations, we have developed Digital Pathology Laboratory (DPLab), a publicly available web-based platform, to assist biomedical research groups, non-technical end users, and clinicians for pathology whole-slide image visualization, annotation, analysis, and sharing via web browsers.Approach.A major advancement of this work is the easy-to-follow methods to reconstruct three-dimension (3D) tissue image volumes by registering two-dimension (2D) whole-slide pathology images of serial tissue sections stained by hematoxylin and eosin (H&E), and immunohistochemistry (IHC). The integration of these serial slides stained by different methods provides cellular phenotype and pathophysiologic states in the context of a 3D tissue micro-environment. DPLab is hosted on a publicly accessible server and connected to a backend computational cluster for intensive image analysis computations, with results visualized, downloaded, and shared via a web interface.Main results.Equipped with an analysis toolbox of numerous image processing algorithms, DPLab supports continued integration of community-contributed algorithms and presents an effective solution to improve the accessibility and dissemination of image analysis algorithms by research communities.Significance.DPLab represents the first step in making next generation tissue investigation tools widely available to the research community, enabling and facilitating discovery of clinically relevant disease mechanisms in a digital 3D tissue space.

Histo-genomics: digital pathology at the forefront of precision medicine

The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.

MaReIA: A Cloud MapReduce Based High Performance Whole Slide Image Analysis Framework

Recent advancements in systematic analysis of high resolution whole slide images have increase efficiency of diagnosis, prognosis and prediction of cancer and important diseases. Due to the enormous sizes and dimensions of whole slide images, the analysis requires extensive computing resources which are not commonly available. Images have to be tiled for processing due to computer memory limitations, which lead to inaccurate results due to the ignorance of boundary crossing objects. Thus, we propose a generic and highly scalable cloud-based image analysis framework for whole slide images. The framework enables parallelized integration of image analysis steps, such as segmentation and aggregation of micro-structures in a single pipeline, and generation of final objects manageable by databases. The core concept relies on the abstraction of objects in whole slide images as different classes of spatial geometries, which in turn can be handled as text based records in MapReduce. The framework applies an overlapping partitioning scheme on images, and provides parallelization of tiling and image segmentation based on MapReduce architecture. It further provides robust object normalization, graceful handling of boundary objects with an efficient spatial indexing based matching method to generate accurate results. Our experiments on Amazon EMR show that MaReIA is highly scalable, generic and extremely cost effective by benchmark tests.

Validation of whole-slide imaging in the primary diagnosis of liver biopsies in a University Hospital

BACKGROUND

Experience in the use of whole slide imaging (WSI) for primary diagnosis is limited and there are no comprehensive reports evaluating this technology in liver biopsy specimens.

AIMS

To determine the accuracy of interpretation of WSI compared with conventional light microscopy (CLM) in the diagnosis of needle liver biopsies.

METHODS

Two experienced liver pathologists blindly analyzed 176 consecutive biopsies from the Pathology Department at the Hospital Clinic of Barcelona. One of the observers performed the initial evaluation with CLM, and the second evaluation with WSI, whereas the second observer performed the first evaluation with WSI and the second with CLM. All slides were digitized in a Ventana iScan HT at 400× and evaluated with the Virtuoso viewer (Roche diagnostics). We used kappa statistics (κ) for two observations.

RESULTS

Intra-observer agreement between WSI and CLM evaluations was almost perfect (96.6%, κ=0.9; 95% confidence interval [95% CI]: 0.9-1 for observer 1, and 90.3%, κ=0.9; 95%CI: 0.8-0.9 for observer 2). Both native and transplantation biopsies showed an almost perfect concordance in the diagnosis.

CONCLUSION

Diagnosis of needle liver biopsy specimens using WSI is accurate. This technology can reliably be introduced in routine diagnosis.

Three-dimensional reconstruction with serial whole-mount sections of oral tongue squamous cell carcinoma: A preliminary study

OBJECTIVES

Margin status and invasion pattern are prognostic factors for oral tongue squamous cell carcinoma (OTSCC). Current methods to identify these factors are limited to 2D observation; it is necessary to explore 3D reconstruction with whole-mount sample to improve the accuracy of analysis. This study aimed to study the tissue preparation, section generation, and 3D reconstruction with whole-mount OTSCC specimen.

STUDY DESIGN

Two OTSCC samples were retrieved from Nanjing Stomatological Hospital, Medical School of Nanjing University. One sample was sliced into 3 equal-sized pieces and subjected to different processing schedules to determine the best method. The second sample was processed accordingly. Serial whole-mount sections of the second sample were generated, stained with HE/anticytokine antibody in intersection manner, and scanned into digital images. Digital images were aligned and reconstructed into 3D images with Hetero Genius Medical Image Manager 3D Pathology Add-On [HGMIM3D].

RESULTS

Successful serial whole-mount sections of comparable quality to traditional sections were generated. Three-dimensional images with serial whole-mount sections were successfully generated.

CONCLUSIONS

Whole-mount histopathological 3D reconstruction of OTSCC was successfully generated, providing a solid foundation for comprehensive margin and invasion analysis. Although future study and improvement were needed, whole-mount histopathological 3D reconstruction proved to be a promising method in OTSCC study.

Development of a Framework for Large Scale Three-Dimensional Pathology and Biomarker Imaging and Spatial Analytics

With the rapid advancement in large-throughput scanning technologies, digital pathology has emerged as platform with promise for diagnostic approaches, but also for high-throughput quantitative data extraction and analysis for translational research. Digital pathology and biomarker images are rich sources of information on tissue architecture, cell diversity and morphology, and molecular pathway activation. However, the understanding of disease in three-dimension (3D) has been hampered by their traditional two-dimension (2D) representations on histologic slides. In this paper, we propose a scalable image processing framework to quantitatively investigate 3D phenotypic and cell-specific molecular features from digital pathology and biomarker images in information- lossless 3D tissue space. We also develop a generalized 3D spatial data management framework with multi-level parallelism and provide a sustainable infrastructure for rapid spatial queries through scalable and efficient spatial data processing. The developed framework can facilitate biomedical research by efficiently processing large-scale, 3D pathology and in-situ biomarker imaging data.

Clinical Applications of Whole-slide Imaging in Anatomic Pathology

The development of whole-slide imaging has paved the way for digitizing of glass slides that are the basis for surgical pathology. This transformative technology has changed the landscape in research applications and education but despite its tremendous potential, its adoption for clinical use has been slow. We review the various niche applications that initiated awareness of this technology, provide examples of clinical use cases, and discuss the requirements and challenges for full adoption in clinical diagnosis. The opportunities for applications of image analysis tools in a workflow will be changed by integration of whole-slide imaging into routine diagnosis.

Digital pathology in the diagnostic setting: beyond technology into best practice and service management

Digital pathology (DP) and whole-slide imaging (WSI) technology have matured substantially over the last few years and there is growing evidence from validation studies that WSI is comparable to glass slides for histopathology diagnosis, although with some limitations, which can be appropriately minimised. Whether the controlled environment of validation studies translates to the same level of robustness when WSI is used in the actual diagnostic setting depends on the technical quality of WSI acquisition and on factors that influence the pre-image acquisition variables including the quality of glass slide inputs, and postimage acquisition variables such as access and use of WSI. The concept of 'DP service management' is introduced to fulfil the holistic needs of a laboratory intending to use the DP solution incorporating WSI for diagnostic purposes. The DP service management team should be an integral part of the diagnostic laboratory as it plays a central role undertaking responsibility to address an extensive range of issues from technical and training to governance and accreditation, hence ensuring a viable and sustainable diagnostic DP integration and usage. The pathologist as a specialist in the field and key decision maker of histopathology diagnoses has the duty and responsibility to acquaint and familiarise with DP and WSI when using the technology, especially on their indications and limitations, so as to take full advantage of these tools to enhance diagnostic quality.

Scalable histopathological image analysis via supervised hashing with multiple features

Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance. In response, we exploit joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features. Supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress multiple high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on 3121 breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.3% classification accuracy within 16.5 ms query time, comparing favorably with traditional methods.