Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive

AbstractThe Quantitative Imaging Network (QIN), supported by the National Cancer Institute, is designed to promote research and development of quantitative imaging methods and candidate biomarkers for the measurement of tumor response in clinical trial settings. An integral aspect of the QIN mission is to facilitate collaborative activities that seek to develop best practices for the analysis of cancer imaging data. The QIN working groups and teams are developing new algorithms for image analysis and novel biomarkers for the assessment of response to therapy. To validate these algorithms and biomarkers and translate theminto clinical practice, algorithms need to be compared and evaluated on large and diverse data sets. Analysis competitions, or “challenges,” are being conducted within the QIN as a means to accomplish this goal. The QIN has demonstrated, through its leveraging of The Cancer Imaging Archive (TCIA), that data sharing of clinical images across multiple sites is feasible and that it can enable and support these challenges. In addition to Digital Imaging and Communications in Medicine (DICOM) imaging data, many TCIA collections provide linked clinical, pathology, and “ground truth” data generated by readers that could be used for further challenges. The TCIA-QIN partnership is a successful model that provides resources for multisite sharing of clinical imaging data and the implementation of challenges to support algorithm and biomarker validation

Overview of the ImageCLEF 2013 medical tasks

In 2013, the tenth edition of the medical task of the Image-CLEF benchmark was organized. For the first time, the ImageCLEFmedworkshop takes place in the United States of America at the annualAMIA (American Medical Informatics Association) meeting even thoughthe task was organized as in previous years in connection with the otherImageCLEF tasks. Like 2012, a subset of the open access collection ofPubMed Central was distributed. This year, there were four subtasks:modality classification, compound figure separation, image–based andcase–based retrieval. The compound figure separation task was includeddue to the large number of multipanel images available in the literatureand the importance to separate them for targeted retrieval. More com-pound figures were also included in the modality classification task tomake it correspond to the distribution in the full database. The retrievaltasks remained in the same format as in previous years but a largernumber of tasks were available for image–based and case–based tasks.This paper presents an analysis of the techniques applied by the tengroups participating 2013 in ImageCLEFmed