Difference between revisions of "Algorithm:MIT"
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= Population Analysis of Anatomical Variability= | = Population Analysis of Anatomical Variability= | ||
| − | Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Algorithm:MIT: | + | Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Algorithm:MIT:Population_Analysis|More...]] |
| − | [[Algorithm:MIT: | + | <font color="red">'''New:'''</font> Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability. |
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| + | [[Algorithm:MIT:Population_Analysis|'''Description''']] - [[Algorithm:MIT:Population_Analysis#Publications|'''Publications''']] - [[Algorithm:MIT:Population_Analysis#Software|'''Software''']] - [[AHM_2006:ProjectsShapeAnalysis|'''AHM 2006''']] | ||
= Groupwise Registration = | = Groupwise Registration = | ||
Revision as of 16:12, 18 April 2007
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Shape- and Atlas-Based Segmentation
The goal of this project is to augment the segmentation process with prior information on the shape of the anatomical structures (shape atlas) learned from previously segmented scans (using, for example, Principal Component Analysis). We are working on methods that integrate the shape atlases with segmentation algorithms.
Tissue Classification
This type of algorithms assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. More...
New: K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Accapted to the Special Issue of Best Selected Papers from MICCAI 06 in Medical Image Analysis More...
New: K.M. Pohl, R. Kikinis, and W.M. Wells. Active Mean Fields: Solving the Mean Field Approximation in the Level Set Framework. Accapted to IPMI 2007. More...
Description - Publications - Software - AHM 2006 - AHM 2007
Boundary Localization
This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. More...
Description - Publications - Software
Registration Regularization
We are interested in the effects of registration regularization on segmentation accuracy in joint registration-segmentation. More...
DTI Analysis and Visualization
Our work in DTI analysis focuses on identifying new ways to provide an interpretation of the white matter connectivity and to utilize the information contained in the DTI images to create more comperehsive models of the brain architecture.
DTI Clustering
The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters. More...
New: Monica E. Lemmond, Lauren J. O'Donnell, Stephen Whalen, Alexandra J. Golby. Characterizing Diffusion Along White Matter Tracts Affected by Primary Brain Tumors. Accepted to HBM 2007. More...
Description - Software - Software - AHM 2006 - PW 2006
Fiber Tract Modeling and Clustering
The goal of this work is to model the shape of the fiber bundles and use this model discription in clustering and statistical analysis of fiber tracts. More...
Description-Publications - Software
DTI-based Segmentation
Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. More...
New: Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006. More...
Description - Publications - Software
fMRI Detection and Analysis
We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. More...
New: Wanmei Ou, Sandy Wells, Polina Golland. Bridging Spatial Regularization And Anatomical Priors in fMRI Detection. In preparation for submission to IEEE TMI. More...
Description - Publications - Software - PW 2006
Population Analysis of Anatomical Variability
Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. More...
New: Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.
Description - Publications - Software - AHM 2006
Groupwise Registration
We are exploring algorithms for groupwise registration of medical data. More...
New: Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.
Collaborations with other groups in NAMIC
- Algorithms:
- Segmentation: joint development of the algorithms and GUI for shape-based hierarchical segmentation with BWH (Kilian Pohl, Steve Pieper).
- Shape Analysis: joint pipeline I/O formulation and development with Kitware (Brad Davis) and UNC (Martin Styner).
- fMRI Detection: joint integration of fMRI detectors into Slicer with BWH (Steve Pieper).
