Daniel J. Tward
Anthony Kolasny
ABSTRACT
The Computational Anatomy Gateway is a software as a service tool for medical imaging researchers to quantify changes in anatomical structures over time, and through the progression of disease. GPU acceleration on the Stampede cluster has enabled the development of new tools, combining advantages of grid based and particle based methods for describing fluid flows, and scaling up analysis from single scans to populations and timeseries. We describe algorithms for estimating average anatomies, and for quantifying atrophy rate over time. We report code performance on different sized datasets, revealing that the number vertices in a triangulated surface presents a bottleneck to our computation. We show results on an example dataset, quantifying atrophy in the entorhinal cortex, a medial temporal lobe brain region whose structure is sensitive changes in early Alzheimer's disease.
- M. F. Beg, M. I. Miller, A. Trouvé, and L. Younes. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International journal of computer vision, 61(2):139--157, 2005. Google Scholar
Digital Library
- H. Braak and E. Braak. Neuropathological stageing of alzheimer-related changes. Acta Neuropathologica, 82:239--259, 1991. http://dx.doi.org/10.1007/BF00308809.Google ScholarCross Ref
- S.-L. Ding and G. W. Van Hoesen. Borders, extent, and topography of human perirhinal cortex as revealed using multiple modern neuroanatomical and pathological markers. Human brain mapping, 31(9):1359--1379, 2010.Google Scholar
- S. Durrleman, S. Allassonnière, and S. Joshi. Sparse adaptive parameterization of variability in image ensembles. Int. J. Comput. Vision, 101(1):161--183, Jan. 2013. Google ScholarDigital Library
- S. Jain, D. J. Tward, D. S. Lee, A. Kolasny, T. Brown, J. T. Ratnanather, M. I. Miller, and L. Younes. Computational anatomy gateway: Leveraging xsede computational resources for shape analysis. In Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, XSEDE '14, pages 54:1--54:6, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- J. Ma, M. I. Miller, and L. Younes. A bayesian generative model for surface template estimation. International Journal of Biomedical Imaging, 2010, 2010. Google ScholarDigital Library
- M. I. Miller, A. Trouvé, and L. Younes. Geodesic shooting for computational anatomy. Journal of mathematical imaging and vision, 24(2):209--228, 2006. Google ScholarDigital Library
- M. I. Miller, L. Younes, and A. Trouve. Diffeomorphometry and geodesic positioning systems for human anatomy. Technology (Singapore World Science), 2:36, 2014. http://dx.doi.org/10.1142/S2339547814500010.Google Scholar
- A. Staniforth and J. Côté. Semi-lagrangian integration schemes for atmospheric models--a review. Mon. Wea. Rev., 119(9):2206--2223, sep 1991.Google ScholarCross Ref
- D. Tward, J. Jovicich, A. Soricelli, G. Frisoni, A. Trouve, L. Younes, and M. Miller. Machine learning in medical imaging: 5th international workshop, mlmi 2014, held in conjunction with miccai 2014, boston, ma, usa, september 14, 2014, proceedings. volume 8679. Springer, 2014.Google Scholar
- D. J. Tward, A. Kolasny, N. Charon, M. I. Miller, and L. Younes. Gpu acceleration on the stampede cluster for the computational anatomy gateway. XSEDE '15, 2015. https://conferences.xsede.org/-/gpu-acceleration-on-the-stampede-cluster-for-the-computational-anaGoogle Scholar
- M. Vaillant and J. Glaunes. Surface matching with currents. Information Processing in Medical Imaging, 19:381--392. Google ScholarDigital Library
- F.-X. Vialard, L. Risser, D. Rueckert, and C. Cotter. 3d image registration via geodesic shooting using and efficient adjoint calculation. Journal International Journal of Computer Vision, 97(2):229--241, April 2012. Google ScholarDigital Library
- L. Younes. Shapes and Diffeomorphisms, volume 171. Series: Applied Mathematical Sciences, 2010.Google Scholar
Recommendations
Computational Anatomy Gateway: Leveraging XSEDE Computational Resources for Shape Analysis
XSEDE '14: Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery EnvironmentComputational Anatomy (CA) is a discipline focused on the quantitative analysis of the variability in biological shape. The Large Deformation Diffeomorphic Metric Mapping (LDDMM) is the key algorithm which assigns computable descriptors of anatomical ...
Performance of Image Matching in the Computational Anatomy Gateway: CPU and GPU Implementations in OpenCL
PEARC '17: Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and ImpactThe Computational Anatomy Gateway is a software as a service tool that provides tools for analysis of structural MRI to the neuroimaging community by calculating diffeomorphic mappings between a user's data and well characterized atlas images. These ...
Functional neuroanatomy of mental rotation
Brain regions involved in mental rotation were determined by assessing increases in fMRI activation associated with increases in stimulus rotation during a mirror-normal parity-judgment task with letters and digits. A letter-digit category judgment task ...
Comments