3D Slicer

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

References in zbMATH (referenced in 7 articles )

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  1. Dunbar, Michelle; O’Brien, Ricky; Froyland, Gary: Optimising lung imaging for cancer radiation therapy (2020)
  2. Lorenzo, Guillermo; Hughes, T. J. R.; Reali, A.; Gomez, H.: A numerical simulation study of the dual role of (5\alpha)-reductase inhibitors on tumor growth in prostates enlarged by benign prostatic hyperplasia via stress relaxation and apoptosis upregulation (2020)
  3. Mendizabal, Andrea; Tagliabue, Eleonora; Brunet, Jean-Nicolas; Dall’Alba, Diego; Fiorini, Paolo; Cotin, Stéphane: Physics-based deep neural network for real-time lesion tracking in ultrasound-guided breast biopsy (2020)
  4. Andrew Beers; James Brown; Ken Chang; Katharina Hoebel; Elizabeth Gerstner; Bruce Rosen; Jayashree Kalpathy-Cramer: DeepNeuro: an open-source deep learning toolbox for neuroimaging (2018) arXiv
  5. Richard Beare; Bradley Lowekamp; Ziv Yaniv: Image Segmentation, Registration and Characterization in R with SimpleITK (2018) not zbMATH
  6. Richard Izzo; David Steinman; Simone Manini; Luca Antiga: The Vascular Modeling Toolkit: A Python Library for the Analysis of Tubular Structures in Medical Images (2018) not zbMATH
  7. Ballarin, Francesco; Faggiano, Elena; Ippolito, Sonia; Manzoni, Andrea; Quarteroni, Alfio; Rozza, Gianluigi; Scrofani, Roberto: Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD-Galerkin method and a vascular shape parametrization (2016)