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| − | Back to [[2008_Progress_Report]]
| + | This latest draft of this report has now been removed from the wiki and is available [[Media:2008_Namic_Progress_Report.doc|here]] in a MS word document for the final submission. If you have any changes to the last version of text, please send these to Tina. The final version will be posted back here by May 30th. If you really need to look at the last wiki version, please click on the history tab of this page and look at the last one edited on May 22nd. [[User:Tkapur|Tkapur]] 14:07, 23 May 2008 (EDT) |
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| − | =Guidelines for preparation=
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| − | *[[2008_Progress_Report#Scientific Report Timeline]] - Main point is that May 15 is the date by which all sections below need to be completed. No extensions are possible.
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| − | *DBPs - If there is work outside of the roadmap projects that you would like to report, you are welcome to create a separate section for it under "Other".
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| − | *The outline for this report is similar to the 2007 report, which is provided here for reference: [[2007_Annual_Scientific_Report]].
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| − | *In preparing summaries for each of the 8 topics in this report, please leverage the detailed pages for projects provided here: [[NA-MIC_Internal_Collaborations]].
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| − | *Publications will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.
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| − | =Introduction (Tannenbaum)=
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| − | The National Alliance for Medical Imaging Computing (NA-MIC) is now in its fourth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The first driving biological projects (DBPs) three years for Center were inspired by schizophrenia research. In the fourth year new DBPs have been added. Three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. In an very new direction, we have added DBP on the prostate: brachytherapy needle positioning robot integration.
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| − | =Clinical Roadmap Projects=
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| − | ==Roadmap Project: Stochastic Tractography for VCFS (Kubicki)==
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| − | ===Overview (Kubicki)===
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| − | ===Algorithm Component (Golland)===
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| − | ===Engineering Component (Davis)===
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| − | ===Clinical Component (Kubicki)===
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| − | ===Additional Information===
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| − | Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard:Brain_Segmentation_Roadmap here on the NA-MIC wiki].
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| − | ==Roadmap Project: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==
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| − | ===Overview (Fichtinger)===
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| − | ===Algorithm Component (Tannenbaum)===
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| − | Currently we attack the segmentation of the prostate in two ways. The first way is a combination of Cellular Automata(CA also called Grow Cut) with Geodesic Active Contour(GAC) methods. While the second is using a ellipsoid to match the prostate in 3D image. The details are given below.
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| − | 1. CA algorithm is used to give a rough segmentation which is fed into GAC for finer tuning. Both algorithm are implemented in 3D. A ITK-Cellular Automata filter, dealing with N-D data, has already been completed and submitted into the NA-MIC SandBox.
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| − | 2. Prostate is usually modeled as an ellipsoid. We try using ellipsoid model, coupled with various local and global segmentation energy definition, to give an fully automatic segmentation.
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| − | ===Engineering Component (Hayes)===
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| − | ===Clinical Component (Fichtinger)===
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| − | ===Additional Information===
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| − | Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU:Roadmap here on the NA-MIC wiki].
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| − | ==Roadmap Project: Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus (Bockholt)==
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| − | ===Overview (Bockholt)===
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| − | ===Algorithm Component (Whitaker)===
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| − | ===Engineering Component (Pieper)===
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| − | ===Clinical Component (Bockholt)===
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| − | ===Additional Information===
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| − | Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND:Roadmap here on the NA-MIC wiki].
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| − | ==Roadmap Project: Cortical Thickness for Autism(Hazlett)==
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| − | ===Overview (Hazlett)===
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| − | ===Algorithm Component (Styner)===
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| − | ===Engineering Component (Miller, Vachet)===
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| − | ===Clinical Component (Hazlett)===
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| − | ===Additional Information===
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| − | Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC:Cortical_Thickness_Roadmap here on the NA-MIC wiki].
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| − | =Four Infrastructure Topics=
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| − | ==Diffusion Image Analysis (Gerig)==
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| − | ===Progress===
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| − | ===Key Investigators===
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| − | ===Additional Information===
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| − | Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:DiffusionImageAnalysis here on the NA-MIC wiki].
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| − | ==Structural Analysis(Tannenbaum)==
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| − | ===Progress===
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| − | Under Structural Analysis, the main topics of research for NAMIC are structural segmentation, registration techniques and shape analysis. These topics are correlated and research in one often finds application in another. For example, shape analysis can yield useful priors for segmentation, or segmentation and registration can provide structural correspondences for use in shape analysis and so on.
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| − | An overview of selected progress highlights under these broad topics follows.
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| − | Structural Segmentation
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| − | * Directional based segmentation
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| − | We have proposed a directional segmentation framework for Direction-weighted Magnetic Resonance imagery by augmenting the Geodesic Active Contour framework with directional information. The classical scalar conformal factor is replaced by a factor that incorporates directionality. We mathematically showed that the optimization problem is well-defined when the factor is a Finsler metric. The calculus of variations or dynamic programming may be used to find the optimal curves. This past year we have applied this methodology in extracting the anchor tract (or centerline) of neural fiber bundles. Further we have applied this in conjunction with the Bayes’ rule into volumetric segmentation for extracting the entire fiber bundles. We have also proposed a novel shape prior in the volumetric segmentation to extract tubular fiber bundles.
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| − | * Stochastic Segmentation
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| − | We have continued work this year on developing new stochastic methods for implementing curvature-driven flows for medical tasks like segmentation. We can now generalize our results to an arbitrary Riemannian surface which includes the geodesic active contours as a special case. We are also implementing the directional flows based on the anisotropic conformal factor described above using this stochastic methodology. Our stochastic snakes’ models are based on the theory of interacting particle systems. This brings together the theories of curve evolution and hydrodynamic limits, and as such impacts our growing use of joint methods from probability and partial differential in image processing and computer vision. We now have working code written in C++ for the two dimensional case and have worked out the stochastic model of the general geodesic active contour model.
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| − | * Statistical PDE Methods for Segmentation
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| − | Our objective is to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE framework. This year, we developed flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows. We have completed a statistically based flow for segmentation using fast marching, and the code has been integrated into Slicer.
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| − | * Atlas Renormalization for Improved Brain MR Image Segmentation
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| − | Atlas-based approaches can automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. However, the accuracy often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this project, we work to improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets shows that the new procedure improves segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies.
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| − | *Multiscale Shape Segmentation Techniques
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| − | The goal of this project is to represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We derived a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia. Our validation shows that our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.
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| − | ===Key Investigators===
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| − | MIT: Kilian Pohl, Sandy Wells, Eric Grimson
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| − | UNC: Martin Styner, Ipek Oguz, Xavier Barbero
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| − | Utah: Ross Whitaker, Guido Gerig, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
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| − | GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Yogesh Rathi, James Malcolm
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| − | Isomics: Steve Pieper
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| − | GE: Bill Lorensen, Jim Miller
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| − | Kitware: Luis Ibanez, Karthik Krishnan,
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| − | UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
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| − | BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
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| − | Dartmouth: Andrew Saykin
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| − | ===Additional Information===
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| − | Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:StructuralImageAnalysis here on the NA-MIC wiki].
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| − | ==fMRI Analysis (Golland)==
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| − | ===Progress===
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| − | ===Key Investigators===
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| − | ===Additional Information===
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| − | Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:fMRIAnalysis here on the NA-MIC wiki].
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| − | ==NA-MIC Kit Theme (Schroeder)==
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| − | ===Progress===
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| − | ===Key Investigators===
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| − | ===Additional Information===
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| − | Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit here on the NA-MIC wiki].
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| − | ==Other Projects==
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| − | Any Project(s) not covered by the 8 sections above
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| − | ==Highlights(Schroeder)==
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| − | ===EM Segmenter or TBD===
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| − | ===DTI progress or TBD===
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| − | ===Outreach (Gollub)===
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| − | ==Impact and Value to Biocomputing (Miller)==
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| − | ===Impact within the Center===
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| − | ===Impact within NIH Funded Research===
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| − | ===National and International Impact===
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| − | ==NA-MIC Timeline (Whitaker)==
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| − | ==Appendix A Publications (Kapur)==
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| − | These will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.
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| − | ==Appendix B EAB Report and Response (Kapur)==
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| − | ===EAB Report===
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| − | ===Response to EAB Report===
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