Biomedical Imaging Informatics

Rubin, Daniel L.; Greenspan, Hayit; Brinkley, James F.

doi:10.1007/978-1-4471-4474-8_9

Cited by 12 publications

(9 citation statements)

References 172 publications

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“…OMERO has a client-server (three-tier) architecture involving a relational database, an image server, and one or more interoperable user interfaces. OMERO is well suited to managing image data and metadata and organizing images so that they can be queried using a visual index or via search 54 (Figure 2a). In its current form, it does not perform sophisticated image analysis.…”

Section: Box 1: Software For Managing and Visualizing Image Datamentioning

confidence: 99%

Online narrative guides for illuminating tissue atlas data and digital pathology images

Rashid

Yuan

Hoffer

et al. 2020

Preprint

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The recent introduction of highly multiplexed imaging of human tissues and tumors promises to fundamentally advance research in tissue biology and human disease. At the same time, histopathology in the clinical setting is undergoing a rapid transition to digital methods. Thus, repositories of imaging data from research and clinical specimens will soon join genomic databases as a means to systematically explore the molecular basis of disease. Even with recent advances in machine learning, experience in anatomic pathology has shown that there is no substitute for expert visual review, annotation, and description of image data. We review the ecosystem of software available for atlas and histopathology images and introduce a new Web-based software tool, Minerva Story, that addresses a critical unmet need. Minerva is an interpretative and interactive guide to complex images organized around guided analysis. We discuss how Minerva and similar software will be integrated into multi-omic browsers for data dissemination of future atlases.

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Section: Box 1: Software For Managing and Visualizing Image Datamentioning

confidence: 99%

Online narrative guides for illuminating tissue atlas data and digital pathology images

Rashid

Yuan

Hoffer

et al. 2020

Preprint

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“…Imagining informatics is concerned with capturing data at the tissue level, including both anatomical (structural) information and, in some circumstances, functional information (Figure 1 C). Imaging plays a central role in disease diagnosis in both human and veterinary medicine, and advances in imaging techniques over the last few decades have had a large impact on diagnostic capabilities by both increasing the detail in which the body can be imaged, and by adding functional information, particularly for the cardiovascular system (Doppler ultrasound [U/S]) and the brain [functional magnetic resonance imaging (fMRI)] ( 5 , 56 ).…”

Section: Imaging Informaticsmentioning

confidence: 99%

“…Optical imaging modalities include light microscopy (e.g., quantitative histopathology), fluorescence microscopy (e.g., confocal microscopy), and electron microscopy. Some structural imaging modalities are organ-specific, such as those developed for ophthalmologic imaging including retinal photography, auto-fluorescence, fluorescein angiography, and optical coherence tomography ( 56 ). Functional imaging modalities infer function by capturing structural changes over time; these modalities include MRS, positron emission tomography, and nuclear medicine imaging, in addition to Doppler U/S and fMRI.…”

Section: Imaging Informaticsmentioning

confidence: 99%

“…Regardless of the modality, biomedical informatics has shifted toward digital capture of structural and functional images. A major goal of the methods of imaging informatics is to extract information about anatomy and to collect features that will be useful for characterizing abnormalities based on morphologic changes ( 56 ). However, all of these images are an unstructured data type, and while computers can readily manage the raw image data, they cannot easily recognize the type of image, annotations on the image, or draw conclusions from the data ( 56 ).…”

Section: Imaging Informaticsmentioning

confidence: 99%

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The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges

McCue

McCoy

2017

Front. Vet. Sci.

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Advances in high-throughput molecular biology and electronic health records (EHR), coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in health care. Big data require collection and analysis of data at an unprecedented scale and represents a paradigm shift in health care, offering (1) the capacity to generate new knowledge more quickly than traditional scientific approaches; (2) unbiased collection and analysis of data; and (3) a holistic understanding of biology and pathophysiology. Big data promises more personalized and precision medicine for patients with improved accuracy and earlier diagnosis, and therapy tailored to an individual’s unique combination of genes, environmental risk, and precise disease phenotype. This promise comes from data collected from numerous sources, ranging from molecules to cells, to tissues, to individuals and populations—and the integration of these data into networks that improve understanding of heath and disease. Big data-driven science should play a role in propelling comparative medicine and “one medicine” (i.e., the shared physiology, pathophysiology, and disease risk factors across species) forward. Merging of data from EHR across institutions will give access to patient data on a scale previously unimaginable, allowing for precise phenotype definition and objective evaluation of risk factors and response to therapy. High-throughput molecular data will give insight into previously unexplored molecular pathophysiology and disease etiology. Investigation and integration of big data from a variety of sources will result in stronger parallels drawn at the molecular level between human and animal disease, allow for predictive modeling of infectious disease and identification of key areas of intervention, and facilitate step-changes in our understanding of disease that can make a substantial impact on animal and human health. However, the use of big data comes with significant challenges. Here we explore the scope of “big data,” including its opportunities, its limitations, and what is needed capitalize on big data in one medicine.

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“…Two major subfields of big data in biomedicine, natural language processing (NLP) and biomedical image processing, are not covered in this review. We refer the reader to the excellent overviews by Meystre et al [1], Friedman and Elhadad [2], Deserno [3], and Rubin et al [4] for an in-depth discussion of these subfields.…”

Section: Introductionmentioning

confidence: 99%

Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics

Peek¹,

Holmes²,

Sun³

2014

Yearb Med Inform

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SummaryObjectives: To review technical and methodological challenges for big data research in biomedicine and health. Methods: We discuss sources of big datasets, survey infrastructures for big data storage and big data processing, and describe the main challenges that arise when analyzing big data. Results: The life and biomedical sciences are massively contributing to the big data revolution through secondary use of data that were collected during routine care and through new data sources such as social media. Efficient processing of big datasets is typically achieved by distributing computation over a cluster of computers. Data analysts should be aware of pitfalls related to big data such as bias in routine care data and the risk of false-positive findings in high-dimensional datasets. Conclusions: The major challenge for the near future is to transform analytical methods that are used in the biomedical and health domain, to fit the distributed storage and processing model that is required to handle big data, while ensuring confidentiality of the data being analyzed.

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Biomedical Imaging Informatics

Cited by 12 publications

References 172 publications

Online narrative guides for illuminating tissue atlas data and digital pathology images

Online narrative guides for illuminating tissue atlas data and digital pathology images

The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges

Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics

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