Algorithm:MIT

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Overview of MIT Algorithms (PI: Polina Golland)

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

MIT Projects

	Nonparametric Models for Supervised Image Segmentation We propose a non-parametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms we develop rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute a final segmentation of the test subject. More... New: M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.
	Segmentation and Visualization for Cardiac Ablation Procedures Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images. More... New: M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.
	Joint Segmentation of Image Ensembles via Latent Atlases Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. More... New: T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.
	Brain Tumor Segmentation and Modeling We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. More... New: B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011. B. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A Generative Model for Brain Tumor Segmentation in Multi-modal Images. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6362:151-159, 2010.
	Segmentation of Nerve and Nerve Ganglia in the Spine Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. More...
	Brain Connectivity Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. More... A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010. A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.
	Quantitative Susceptibility Mapping There is increasing evidence that excessive iron deposition in specific regions of the brain is associated with neurodegenerative disorders such as Alzheimer's and Parkinson's disease. The role of iron in the pathogenesis of these diseases remains unknown and is difficult to determine without a non-invasive method to quantify its concentration in-vivo. Since iron is a ferromagnetic substance, changes in iron concentration result in local changes in the magnetic susceptibility of tissue. In magnetic resonance imaging (MRI) experiments, differences in magnetic susceptibility cause perturbations in the local magnetic field, which can be computed from the phase of the MR signal.More... New: Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.
	Functional connectivity atlases and tumors We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. More... New: G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011. G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

	Learning Task-Optimal Registration Cost Functions We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. More... B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.
	Spherical Demons: Fast Surface Registration We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, More... B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.
	Improving fMRI Analysis using Supervised and Unsupervised Learning One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. More... D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010. D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010. D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.
	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... W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.
	Multi-variate activation detection We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information. More... New: G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.
	Fieldmap-Free EPI Distortion Correction In this project we aim to improve the EPI distortion correction algorithms. More...

	Bayesian Segmentation of MRI Images The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. More...
	Multimodal Atlas In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster, is used to compute multiple atlases for a given population. More...
	Groupwise Registration We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. More...
	Optimal Atlas Regularization in Image Segmentation We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image fidelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application. More...
	Shape Analysis With Overcomplete Wavelets In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development More...
	Fiber Tract Modeling, Clustering, and Quantitative Analysis The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. More...
	Shape Based Segmentation and Registration This type of algorithm 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...
	Joint Registration and Segmentation of DWI Fiber Tractography The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. More...
	DTI Fiber Clustering and Fiber-Based Analysis The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. More...
	DTI-based Segmentation Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. More...
	Stochastic Tractography This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. More...
	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...