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As part of the DICOM Standards for Small Animal Imaging; Use of Informatics for Co-clinical Trials sub-project, the long-term goal is to generate DICOM-compliant images for small animal research. Micro AIM MicroAIM (µAIM) is currently in development to serve the unique needs of this domain.
The following table presents the data that the CTIIP team is integrating through various means. This integration relies on the expansion of software features and on the application of data standards, as described in subsequent sections of this document.
| Domain | Data Set | Applicable Standard |
|---|---|---|
| Clinical Imaging | The Cancer Genome Atlas (TCGA) clinical and molecular data | DICOM |
| Clinical Imaging | The Cancer Imaging Archive (TCIA) in vivo imaging data | DICOM |
| Pre-Clinical | Small animal models | N/A MicroAIM in development |
| Digital Pathology | caMicroscope | DICOM |
| All | Annotations and markup on images | AIM |
Digital Pathology and Integrated Query System
The goal of this foundational sub-project is to create a digital pathology image server that can accept images from multiple domains and run integrative queries on that data. Using this server, data can be selected from distinct imaging sources and made accessible for image algorithms. The first data sets that are being integrated on this image server, which is an extended version of caMicroscope, are TCGA and TCIA.
The TCGA project is producing a comprehensive genomic characterization and analysis of 200 types of cancer and providing this information to the research community. TCIA and the underlying National Biomedical Image Archive (NBIA) manage well-curated, publicly-available collections of medical image data. The linkages between TCGA and TCIA are valuable to researchers who want to study diagnostic images associated with the tissue samples sequenced by TCGA.
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To address these limitations, the CTIIP team is developing a unified query interface to make it easier to analyze data from different research domains . This interface, plus related open-source software and data standards, would then be applied to represented by TCGA, TCIA, and co-clinical, /small animal model data, and . It will also provide a common platform and data engine for the hosting of “pilot challenges.” These pilot challenges will proactively facilitate advance biological and clinical research across the clinical, pre-clinical, and digital pathology imaging research domains.
TCIA has released an Application Programmatic Interface that provides a REST API to TCIA metadata and image collections. This API is built using a middleware platform called Bindaas.
disciplines.
Digital Pathology
Digital pathology, unlike its more mature radiographic counterpart, has yet to standardize on a single storage and transport media. In addition, each pathology-imaging vendor produces its own image management systems, making image analysis systems proprietary and not standardized. The result is that images produced on different systems cannot be analyzed via the same mechanisms. Not only does this lack of standards and the dominance of proprietary formats impact digital pathology, but it prevents digital pathology data from integrating with radiographic, genomic, and proteomic data.
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Image annotations also require standards so that they can be read across domains by different imaging disciplines along with the rest of the image data. caMicroscope will also be extended to include standards-based image annotation using the Annotation Image Markup (AIM) standard.
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We are exploring the standardization of informatics. Use all the tools we have to create standard informatics to compare patient to animal data. We are using the available standards: DICOM, AIM, micro AIM. Fundamental to integrative queries.
All three research domains disciplines need an imaging archive that can be leveraged for integration across multiple data types and sources. For example, TCGA program has the goal of producing a comprehensive genomic characterization and analysis of 200 types of cancer and providing this information to the research community. TCIA and the underlying National Biomedical Image Archive (NBIA) software stack were created to manage well-curated, publicly-available collections of medical image data, including diagnostic images associated with the tissue samples sequenced by TCGA. TCIA currently supports over 40 active research groups including researchers who are exploiting the existing linkages between TCGA and TCIA. TCIA has recently released an Application Program Interface (API) that provides a REST API to TCIA metadata and image collections. This API is built using a middleware platform called Bindaas,and this API is being designed to support federation of multiple information repositories using the concepts of a data mashups. This infrastructure can be expanded to include more data types and additional integration, and provide analytic and decision support, which will act as a foundation for a broader set of novel community research projects.
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