Debra Willrett

3D Markup of Radiological Images in ePAD, a Web-Based Image Annotation Tool

Quantitative and semantic information about medical images are vital parts of a radiological report. However, current image viewing systems do not record it in a format that permits machine interpretation. The ePAD tool can generate machine-computable image annotations on 2D images as part of a radiologists routine workflow. The tool has been evaluated in image studies with good results. Since ePAD currently only provides 2D visualization and annotation of images, we developed a plugin to ePAD for the visualization of volumetric image datasets, using the three planes: axial, frontal and sagittal. A study with 6 radiologists was carried out to determine the best interface for also marking 3D ROIs. Video prototypes were created for 3 options: join pixels based on intensity similarity, detect borders around image features, and paint ROIs using a spheric 3D cursor. The 3D cursor was the preferred option. We present these results and also show the final 3D cursor implementation.

An Open Repository Model for Acquiring Knowledge About Scientific Experiments

The availability of high-quality metadata is key to facilitating discovery in the large variety of scientific datasets that are increasingly becoming publicly available. However, despite the recent focus on metadata, the diversity of metadata representation formats and the poor support for semantic markup typically result in metadata that are of poor quality. There is a pressing need for a metadata representation format that provides strong interoperation capabilities together with robust semantic underpinnings. In this paper, we describe such a format, together with open-source Web-based tools that support the acquisition, search, and management of metadata. We outline an initial evaluation using metadata from a variety of biomedical repositories.

Using the Semantic Web and Web Apps to Connect Radiologists and Oncologists

Medical imaging plays an important role in the diagnosis, prognosis and treatment of cancer. Quantitative and qualitative data about medical images are vital components of a radiological report and are very important to the oncologist that requests the radiological exams. However, traditional methods to register these data are inefficient and error prone. The use of unstructured free text in radiology reports makes it impossible to perform even simple calculations, such as changes in lesion dimensions. It also makes the aggregated analysis of many reports difficult. Free text reports lack a reference to the image regions of the finds they refer to and are not machine-computable. This paper proposes a method to provide support for collaborative work among radiologists and oncologists (providing care or taking part in clinical trials) using an imaging web tool, ePAD, to generate structured radiology reports that can be machine-computable. It also shows how ePAD uses Rad Lex ontology terms and the Annotation and Image Markup (AIM) language (and templates) to generate the reports.

The CAIRR Pipeline for Submitting Standards-Compliant B and T Cell Receptor Repertoire Sequencing Studies to the National Center for Biotechnology Information Repositories

The adaptation of high-throughput sequencing to the B cell receptor and T cell receptor has made it possible to characterize the adaptive immune receptor repertoire (AIRR) at unprecedented depth. These AIRR sequencing (AIRR-seq) studies offer tremendous potential to increase the understanding of adaptive immune responses in vaccinology, infectious disease, autoimmunity, and cancer. The increasingly wide application of AIRR-seq is leading to a critical mass of studies being deposited in the public domain, offering the possibility of novel scientific insights through secondary analyses and meta-analyses. However, effective sharing of these large-scale data remains a challenge. The AIRR community has proposed minimal information about adaptive immune receptor repertoire (MiAIRR), a standard for reporting AIRR-seq studies. The MiAIRR standard has been operationalized using the National Center for Biotechnology Information (NCBI) repositories. Submissions of AIRR-seq data to the NCBI repositories typically use a combination of web-based and flat-file templates and include only a minimal amount of terminology validation. As a result, AIRR-seq studies at the NCBI are often described using inconsistent terminologies, limiting scientists’ ability to access, find, interoperate, and reuse the data sets. In order to improve metadata quality and ease submission of AIRR-seq studies to the NCBI, we have leveraged the software framework developed by the Center for Expanded Data Annotation and Retrieval (CEDAR), which develops technologies involving the use of data standards and ontologies to improve metadata quality. The resulting CEDAR-AIRR (CAIRR) pipeline enables data submitters to: (i) create web-based templates whose entries are controlled by ontology terms, (ii) generate and validate metadata, and (iii) submit the ontology-linked metadata and sequence files (FASTQ) to the NCBI BioProject, BioSample, and Sequence Read Archive databases. Overall, CAIRR provides a web-based metadata submission interface that supports compliance with the MiAIRR standard. This pipeline is available at http://cairr.miairr.org, and will facilitate the NCBI submission process and improve the metadata quality of AIRR-seq studies.

CEDAR OnDemand: a browser extension to generate ontology-based scientific metadata

BackgroundPublic biomedical data repositories often provide web-based interfaces to collect experimental metadata. However, these interfaces typically reflect the ad hoc metadata specification practices of the associated repositories, leading to a lack of standardization in the collected metadata. This lack of standardization limits the ability of the source datasets to be broadly discovered, reused, and integrated with other datasets. To increase reuse, discoverability, and reproducibility of the described experiments, datasets should be appropriately annotated by using agreed-upon terms, ideally from ontologies or other controlled term sources.ResultsThis work presents “CEDAR OnDemand”, a browser extension powered by the NCBO (National Center for Biomedical Ontology) BioPortal that enables users to seamlessly enter ontology-based metadata through existing web forms native to individual repositories. CEDAR OnDemand analyzes the web page contents to identify the text input fields and associate them with relevant ontologies which are recommended automatically based upon input fields’ labels (using the NCBO ontology recommender) and a pre-defined list of ontologies. These field-specific ontologies are used for controlling metadata entry. CEDAR OnDemand works for any web form designed in the HTML format. We demonstrate how CEDAR OnDemand works through the NCBI (National Center for Biotechnology Information) BioSample web-based metadata entry.ConclusionCEDAR OnDemand helps lower the barrier of incorporating ontologies into standardized metadata entry for public data repositories. CEDAR OnDemand is available freely on the Google Chrome store https://chrome.google.com/webstore/search/CEDAROnDemand

The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments

The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developed-the CEDAR Workbench-is a suite of Web-based tools and REST APIs that allows users to construct metadata templates, to fill in templates to generate high-quality metadata, and to share and manage these resources. The CEDAR Workbench provides a versatile, REST-based environment for authoring metadata that are enriched with terms from ontologies. The metadata are available as JSON, JSON-LD, or RDF for easy integration in scientific applications and reusability on the Web. Users can leverage our APIs for validating and submitting metadata to external repositories. The CEDAR Workbench is freely available and open-source.

CEDAR's predictive data entry: easier and faster creation of high-quality metadata

The Value Recommender system is the first of a planned set of intelligent authoring components in the CEDAR system. Future efforts will concentrate on deeper analyses of metadata to discover more interesting relationships between metadata fields, which will then drive new tools to assist biomedical investigators when annotating their data. The Value Recommender supports both freetext values and controlled terms. In this particular example, the system suggests terms from the Human Disease Ontology (DOID).