3D Slicer as an image computing platform for the Quantitative Imaging Network

• Published in: Magnetic resonance imaging Vol. 30; no. 9; pp. 1323 - 1341
• Main Authors: Fedorov, Andriy, Beichel, Reinhard, Kalpathy-Cramer, Jayashree, Finet, Julien, Fillion-Robin, Jean-Christophe, Pujol, Sonia, Bauer, Christian, Jennings, Dominique, Fennessy, Fiona, Sonka, Milan, Buatti, John, Aylward, Stephen, Miller, James V, Pieper, Steve, Kikinis, Ron
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01-11-2012
• Subjects: Automation; Biomarkers - metabolism; Brain; Brain Neoplasms - pathology; Cancer imaging; Cancer treatment response; Databases, Factual; Diagnostic Imaging - methods; Glioblastima; Glioblastoma - pathology; Head and neck; Head and Neck Neoplasms - pathology; Humans; Image analysis; Imaging biomarkers; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medical Informatics - methods; MRI; PET; Positron-Emission Tomography - methods; Prostate; Prostatic Neoplasms - pathology; Quantitative imaging; Radiology; Software; Software tools; Tomography, X-Ray Computed - methods; Glioblastima; Brain; Medical imaging; MRI; CT; Cancer treatment response; Image analysis; Head and neck; Quantitative imaging; Imaging biomarkers; Cancer imaging; Software tools; Prostate; PET
• ISSN: 0730-725X; 1873-5894
• DOI: 10.1016/j.mri.2012.05.001

Abstract Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.