Development of an Image Data Set Class: Its Role in Biomedical Imaging and Neuroimaging Research

Biomedical imaging is a widely used tool both clinically and for research. Though a standard digital format has existed in the biomedical imaging world across modalities for decades in the form of the DICOM specification, a formal representation of the kinds of data present in a biomedical image (acquired from CT, PET, MRI, etc.) is notably absent from biomedical ontologies, and annotation of biomedical imaging data is hindered by decentralization. This has contributed to the creation of large and unsorted biomedical imaging silos, preventing clinical and translational researchers from effectively sharing and analyzing their data. We present here the ‘image data set’ class, along with the ‘image data set analysis’ class, which we have developed to capture the processes of acquisition, annotation, and analysis of biomedical imaging data in an effort to better harness otherwise-latent imaging datasets. The ‘image data set’ class and several of its children are being contributed to OBI and originate from MRIO, an application ontology used to guide a neuroinformatics platform working to automate analysis of large MRI datasets and facilitate the translation of neuroimaging research into clinical science.