Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms
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Developing both graphical and command-line user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing. We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software—BioImage Suite (bioimagesuite.org).
- Anderson, E., et al. (1999). Lapack user’s guide. SIAM.
- Beck, K., & Andres, C. (2004). Extreme programming explained: Embrace change (2nd ed.). Addison-Wesley Professional.
- Coronato, A., De Pietro, G, & Marra, I. (2006). An open-source software architecture for immersive medical imaging. In Proceedings of the IEEE international conference on virtual environments, HCI and measurement systems.
- Ibanez, L., & Schroeder, W. (2003). The ITK software guide: The insight segmentation and registration toolkit. Kitware, Inc., Albany, NY. www.itk.org.
- Lucas, B.C., Bogovic, J. A., Carass, A., Bazin, P.-L., Prince, J. L., Pham, D. L., et al. (2010). The java image science toolkit (jist) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics, 8, 5–17. CrossRef
- Martin, K., & Hoffman, B. (2009). Mastering CMake. Kitware, Inc.
- Meltzer, J. A., Zaveri, H. P., Goncharova, I. I., Distasio, M. M., Papademetris, X., Spencer, S. S., et al. (2008). Effects of working memory load on oscillatory power in human intracranial EEG. Cerebral Cortex, 18, 1843–1855. CrossRef
- NVIDIA (2007). NVIDIA compute unified device architecture (CUDA).
- Papademetris, X., DeLorenzo, C., Flossmann, S., Neff, M., Vives, K., Spencer, D., et al. (2009a). From medical image computing to computer-aided intervention: Development of a research interface for image-guided navigation. In International journal of medical robotics and computer assisted surgery (Vol. 5, pp. 147–157).
- Papademetris, X., Jackowski, M., Joshi, A., Scheinost, D., Murphy, I., Constable, R. T., et al. (2009b). The BioImage suite module description manual. A manual for the BioImage Suite project.
- Petersen, K. F., Dufour, S., Savage, D. B., Bilz, S., Solomon, G., Yonemitsu, S., et al. (2007). The role of skeletal muscle insulin resistance in the pathogenesis of the metabolic syndrome. Proceedings of the National Academy of Sciences of the United States of America, 104, 12587–12594. CrossRef
- Pieper, S., Halle, M., & Kikinis, R. (2004). 3D slicer. IEEE international symposium on biomedical imaging ISBI 2004.
- Scheinost, D., Blumenfeld, H., & Papademetris, X. (2009). An improved unbiased method for diffspect quantification in epilepsy. IEEE international symposium on biomedical imaging ISBI 2009.
- Schroeder, W., Martin, K., & Lorensen, B. (2003). The visualization toolkit: An object-oriented approach to 3D Graphics. Kitware, Inc., Albany, NY. www.vtk.org.
- Shen, R., Boulanger, P., & Noga, M. (2008). Medvis: A real-time immersive visualization environment for the exploration of medical volumetric data. In Proceedings of the fifth international conference on biomedical visualization (pp. 63–68).
- Smith, C. (2000). [Incr-tcl/tk] from the ground up. McGraw-Hill.
- Taksali, S. E., Caprio, S., Dziura, J., Dufour, S., Cali, A. M., Goodman, T. R., et al. (2008). High visceral and low abdominal subcutaneous fat stores in the obese adolescent: A determinant of an adverse metabolic phenotype. Diabetes, 57, 367–371. CrossRef
- Rex, D. E., Ma, J. Q., & Toga, A. W. (2003). The LONI pipeline processing environment. NeuroImage, 19(3), 1033–1048. CrossRef
- VWware Server (2005) http://www.vmware.com/products/server/.
- Wolf, I., Vetter, M., Wegner, I., Bottger, T., Nolden, M., Schobinger, M., et al. (2005). The medical imaging interaction toolkit. In Medical image analysis (pp. 594–604).
- Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms
Volume 9, Issue 1 , pp 69-84
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- Neuroimaging software
- Open source medical imaging software
- Software development framework
- Comprehensive software testing
- Author Affiliations
- 1. Department of Diagnostic Radiology, Yale University, 300 Cedar Street, New Haven, CT, 06520, USA
- 2. Biomedical Engineering, Yale University, 300 Cedar Street, New Haven, CT, 06520, USA
- 3. GE Healthcare, Tokyo, Japan
- 4. Electrical Engineering, Yale University, 300 Cedar Street, New Haven, CT, 06520, USA