Feasibility analysis of high resolution tissue image registration using 3-D synthetic data

Vision 20/20: Molecular-guided surgical oncology based upon tumor metabolism or immunologic phenotype: Technological pathways for point of care imaging and intervention

Surgical guidance with fluorescence has been demonstrated in individual clinical trials for decades, but the scientific and commercial conditions exist today for a dramatic increase in clinical value. In the past decade, increased use of indocyanine green based visualization of vascular flow, biliary function, and tissue perfusion has spawned a robust growth in commercial systems that have near-infrared emission imaging and video display capabilities. This recent history combined with major preclinical innovations in fluorescent-labeled molecular probes, has the potential for a shift in surgical practice toward resection guidance based upon molecular information in addition to conventional visual and palpable cues. Most surgical subspecialties already have treatment management decisions partially based upon the immunohistochemical phenotype of the cancer, as assessed from molecular pathology of the biopsy tissue. This phenotyping can inform the surgical resection process by spatial mapping of these features. Further integration of the diagnostic and therapeutic value of tumor metabolism sensing molecules or immune binding agents directly into the surgical process can help this field mature. Maximal value to the patient would come from identifying the spatial patterns of molecular expression in vivo that are well known to exist. However, as each molecular agent is advanced into trials, the performance of the imaging system can have a critical impact on the success. For example, use of pre-existing commercial imaging systems are not well suited to image receptor targeted fluorophores because of the lower concentrations expected, requiring orders of magnitude more sensitivity. Additionally the imaging system needs the appropriate dynamic range and image processing features to view molecular probes or therapeutics that may have nonspecific uptake or pharmacokinetic issues which lead to limitations in contrast. Imaging systems need to be chosen based upon objective performance criteria, and issues around calibration, validation, and interpretation need to be established before a clinical trial starts. Finally, as early phase trials become more established, the costs associated with failures can be crippling to the field, and so judicious use of phase 0 trials with microdose levels of agents is one viable paradigm to help the field advance, but this places high sensitivity requirements on the imaging systems used. Molecular-guided surgery has truly transformative potential, and several key challenges are outlined here with the goal of seeing efficient advancement with ideal choices. The focus of this vision 20/20 paper is on the technological aspects that are needed to be paired with these agents.

Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies

Background

We describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands.

Results

The proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system.

Conclusions

Our work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.

Big Data: the challenge for small research groups in the era of cancer genomics

In the past decade, cancer research has seen an increasing trend towards high-throughput techniques and translational approaches. The increasing availability of assays that utilise smaller quantities of source material and produce higher volumes of data output have resulted in the necessity for data storage solutions beyond those previously used. Multifactorial data, both large in sample size and heterogeneous in context, needs to be integrated in a standardised, cost-effective and secure manner. This requires technical solutions and administrative support not normally financially accounted for in small- to moderate-sized research groups. In this review, we highlight the Big Data challenges faced by translational research groups in the precision medicine era; an era in which the genomes of over 75 000 patients will be sequenced by the National Health Service over the next 3 years to advance healthcare. In particular, we have looked at three main themes of data management in relation to cancer research, namely (1) cancer ontology management, (2) IT infrastructures that have been developed to support data management and (3) the unique ethical challenges introduced by utilising Big Data in research.

Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images

Technological advances in computing, imaging, and genomics have created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. Slide scanning devices are now capable of rapidly producing massive digital image archives that capture histological details in high resolution. Commensurate advances in computing and image analysis algorithms enable mining of archives to extract descriptions of histology, ranging from basic human annotations to automatic and precisely quantitative morphometric characterization of hundreds of millions of cells. These imaging capabilities represent a new dimension in tissue-based studies, and when combined with genomic and clinical endpoints, can be used to explore biologic characteristics of the tumor microenvironment and to discover new morphologic biomarkers of genetic alterations and patient outcomes. In this paper, we review developments in quantitative imaging technology and illustrate how image features can be integrated with clinical and genomic data to investigate fundamental problems in cancer. Using motivating examples from the study of glioblastomas (GBMs), we demonstrate how public data from The Cancer Genome Atlas (TCGA) can serve as an open platform to conduct in silico tissue-based studies that integrate existing data resources. We show how these approaches can be used to explore the relation of the tumor microenvironment to genomic alterations and gene expression patterns and to define nuclear morphometric features that are predictive of genetic alterations and clinical outcomes. Challenges, limitations, and emerging opportunities in the area of quantitative imaging and integrative analyses are also discussed.

Enabling Ontology Based Semantic Queries in Biomedical Database Systems

There is a lack of tools to ease the integration and ontology based semantic queries in biomedical databases, which are often annotated with ontology concepts. We aim to provide a middle layer between ontology repositories and semantically annotated databases to support semantic queries directly in the databases with expressive standard database query languages. We have developed a semantic query engine that provides semantic reasoning and query processing, and translates the queries into ontology repository operations on NCBO BioPortal. Semantic operators are implemented in the database as user defined functions extended to the database engine, thus semantic queries can be directly specified in standard database query languages such as SQL and XQuery. The system provides caching management to boosts query performance. The system is highly adaptable to support different ontologies through easy customizations. We have implemented the system DBOntoLink as an open source software, which supports major ontologies hosted at BioPortal. DBOntoLink supports a set of common ontology based semantic operations and have them fully integrated with a database management system IBM DB2. The system has been deployed and evaluated with an existing biomedical database for managing and querying image annotations and markups (AIM). Our performance study demonstrates the high expressiveness of semantic queries and the high efficiency of the queries.

Registration of histological whole slide images guided by vessel structures

Introduction:

The registration of histological whole slide images is an important prerequisite for modern histological image analysis. A partial reconstruction of the original volume allows e.g. colocalization analysis of tissue parameters or high-detail reconstructions of anatomical structures in 3D.

Methods:

In this paper, we present an automatic staining-invariant registration method, and as part of that, introduce a novel vessel-based rigid registration algorithm using a custom similarity measure. The method is based on an iterative best-fit matching of prominent vessel structures.

Results:

We evaluated our method on a sophisticated synthetic dataset as well as on real histological whole slide images. Based on labeled vessel structures we compared the relative differences for corresponding structures. The average positional error was close to 0, the median for the size change factor was 1, and the median overlap was 0.77.

Conclusion:

The results show that our approach is very robust and creates high quality reconstructions. The key element for the resulting quality is our novel rigid registration algorithm.