Accurate and unambiguous communication of derived image-related information is of critical for the emerging applications of quantitative imaging. Actively researched areas in quantitative imaging include segmentation of normal organs and pathology, spatial registration across time-points and modalities, and performing measurements. Digital Imaging and Communication in Medicine (DICOM), first released in 1993 is the standard used ubiquitously in the commercial medical devices that are used in clinical radiology, dentistry, cardiology and nuclear medicine imaging systems. It is also increasingly used in non-radiological specialties, particularly for visible light imaging, including histopathology, gastroenterology, dermatology and ophthalmology. DICOM defines both communication interfaces and data formats for images and image-related information (including measurements, ROIs and segmentations). The data formats (object classes) can be used separately (e.g., stored on media) from the communication interfaces. While a variety of DICOM object classes exist for describing such derived image-related information, thus far they found very limited acceptance both in the academic community and among the manufacturers of radiology workstations implementing quantitative image analysis methods. As a result, longitudinal tracking, comparison of methods, and secondary analyses are challenging, while quantitative imaging assessment of patients is difficult or impossible to perform across different platforms. We aim to address these issues by communicating image segmentation results using the object classes defined by the standard.