P.T. Fletcher, J. Moeller, J.M. Phillips, S. Venkatasubramanian. Computing Hulls In Positive Definite Space, In In Proceedings of the 19th Fall Workshop on Computational Geometry, November, 2009.
T. Fogal, J. Krüger. Size Matters - Revealing Small Scale Structures in Large Datasets, In Proceedings of the World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, IFMBE Proceedings, Vol. 25/13, Springer Berlin Heidelberg, pp. 41--44. 2009.
C.D. Hansen, C.R. Johnson, V. Pascucci, C.T. Silva. Visualization for Data-Intensive Science, In The Fourth Paradigm: Data-Intensive Science, Edited by S. Tansley and T. Hey and K. Tolle, Microsoft Research, pp. 153--164. 2009.
Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees, In JoVE, Vol. 29, 2009.B.M. Isaacson, J.G. Stinstra, R.S. MacLeod, J.B. Webster, J.P. Beck, R.D. Bloebaum.
Many kinds of neuroscience data are being acquired regarding the dynamic behaviour and phenotypic diversity of nerve cells. But as the size, complexity and numbers of 3D neuroanatomical datasets grow ever larger, the need for automated detection and analysis of individual neurons takes on greater importance. We describe here a method that detects and identifies neurons within confocal image stacks acquired from the zebrafish brainstem. The first step is to create a template that incorporates the location of all known neurons within a population - in this case the population of reticulospinal cells. Once created, the template is used in conjunction with a sequence of algorithms to determine the 3D location and identity of all fluorescent neurons in each confocal dataset. After an image registration step, neurons are segmented within the confocal image stack and subsequently localized to specific locations within the brainstem template - in many instances identifying neurons as specific, individual reticulospinal cells. This image-processing sequence is fully automated except for the initial selection of three registration points on a maximum projection image. In analysing confocal image stacks that ranged considerably in image quality, we found that this method correctly identified on average approximately 80% of the neurons (if we assume that manual detection by experts constitutes 'ground truth'). Because this identification can be generated approximately 100 times faster than manual identification, it offers a considerable time savings for the investigation of zebrafish reticulospinal neurons. In addition to its cell identification function, this protocol might also be integrated with stereological techniques to enhance quantification of neurons in larger databases. Our focus has been on zebrafish brainstem systems, but the methods described should be applicable to diverse neural architectures including retina, hippocampus and cerebral cortex.
J. Krüger, T. Fogal. Focus and Context - Visualization without the Complexity, In Proceedings of the World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, IFMBE Proceedings, Vol. 25/13, Springer Berlin Heidelberg, pp. 44--48. 2009.
S. Lew, C.H. Wolters, T. Dierkes, C. Röer, R.S. MacLeod. Accuracy and run-time comparison for different potential approaches and iterative solvers in finite element method based EEG source analysis, In Applied Numerical Mathematics, Vol. 59, pp. 1970--1988. 2009.
S. Lew, C.H. Wolters, A. Anwander, S. Makeig, R.S. MacLeod. Improved EEG Source Analysis Using Low-Resolution Conductivity Estimation in a Four-Compartment Finite Element Head Model, In Human Brain Mapping, Vol. 30, pp. 2862--2878. 2009.
R.S. MacLeod, J.G. Stinstra, S. Lew, R.T. Whitaker, D.J. Swenson, M.J. Cole, J. Krüger, D.H. Brooks, C.R. Johnson. Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples, In Philosophical Transactions of The Royal Society A, Mathematical, Physical & Engineering Sciences, Vol. 367, No. 1896, pp. 2293--2310. 2009.
H.G. Martinez, S.I. Prajapati, C.A. Estrada, F. Jimenez, M.P. Quinones, I. Wu, A. Bahadur, A. Sanderson, C.R. Johnson, M. Shim, C. Keller, S.S. Ahuja. Microscopic Computed Tomography Based Virtual Histology for Visualization and Morphometry of Atherosclerosis in Diabetic Apolipoprotein E Mutant Mice, In Circulation: Journal of the American Heart Association, Vol. 120, No. 9, pp. 821--822. 2009.
H. Martinez, S. Prajapati, C. Estrada, F. Jimenez, I. Wu, A. Bahadur, A. Sanderson, C.R. Johnson, M. Shim, C. Keller, S. Ahuja. Microscopic Computed Tomography–Based Virtual Histology for Visualization and Morphometry of Atherosclerosis in Diabetic Apolipoprotein E Mutant Mice, In Circulation, Vol. 120, No. 821--822, 2009.
M. Milanic, V. Jazbinsek, D.F. Wang, J. Sinstra, R.S. Macleod, D.H. Brooks, R. Hren. Evaluation of Approaches of Solving Electrocardiographic Imaging Problem, In Proceeding of Computers in Cardiology 2010, Park City, Utah, September, 2009.
R.S. Oakes, T.J. Badger, E.G. Kholmovski, N. Akoum, N.S. Burgon, E.N. Fish, J.J. Blauer, S.N. Rao, E.V. DiBella, N.M. Segerson, M. Daccarett, J. Windfelder, C.J. McGann, D.L. Parker, R.S. MacLeod, N.F. Marrouche. Detection and quantification of left atrial structural remodeling with delayed-enhancement magnetic resonance imaging in patients with atrial fibrillation, In Circulation, Vol. 119, No. 13, pp. 1758--1767. 2009.
N.M. Segerson, M. Daccarett, T.J. Badger, A. Shabaan, N. Akoum, E.N. Fish, S. Rao, N.S. Burgon, Y. Adjei-Poku, E.G. Kholmovski, S. Vijayakumar, E.V.R. Dibella, R.S. Macleod, N.F. Marrouche. Magnetic Resonance Imaging-Confirmed Ablative Debulking of the Left Atrial Posterior Wall and Septum for Treatment of Persistent Atrial Fibrillation: Rationale and Initial Experience, In Journal of Cardiovascular Electrophysiology, Vol. 21, No. 2, pp. 126--132. 2009.
J.F. Shepherd, C.R. Johnson. Hexahedral Mesh Generation for Biomedical Models in SCIRun, In Engineering with Computers, Vol. 25, No. 1, pp. 97--114. 2009.