Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Image Analysis

SCI's imaging work addresses fundamental questions in 2D and 3D image processing, including filtering, segmentation, surface reconstruction, and shape analysis. In low-level image processing, this effort has produce new nonparametric methods for modeling image statistics, which have resulted in better algorithms for denoising and reconstruction. Work with particle systems has led to new methods for visualizing and analyzing 3D surfaces. Our work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs). Application areas include medical image analysis, biological image processing, defense, environmental monitoring, and oil and gas.


ross

Ross Whitaker

Segmentation
sarang

Sarang Joshi

Shape Statistics
Segmentation
Brain Atlasing
tolga

Tolga Tasdizen

Image Processing
Machine Learning
tom

Tom Fletcher

Shape Statistics
Diffusion Tensor Analysis
chris

Chris Johnson

Diffusion Tensor Analysis



Image Analysis Project Sites:


Publications in Image Analysis:


Optimal parameter map estimation for shape representation: A generative approach
S. Elhabian, P. Agrawal, R. Whitaker. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 660--663. April, 2016.
DOI: 10.1109/isbi.2016.7493353

Probabilistic label maps are a useful tool for important medical image analysis tasks such as segmentation, shape analysis, and atlas building. Existing methods typically rely on blurred signed distance maps or smoothed label maps to model uncertainties and shape variabilities, which do not conform to any generative model or estimation process, and are therefore suboptimal. In this paper, we propose to learn probabilistic label maps using a generative model on given set of binary label maps. The proposed approach generalizes well on unseen data while simultaneously capturing the variability in the training samples. Efficiency of the proposed approach is demonstrated for consensus generation and shape-based clustering using synthetic datasets as well as left atrial segmentations from late-gadolinium enhancement MRI.



Compressive sensing based Q-space resampling for handling fast bulk motion in hardi acquisitions
S. Elhabian, C. Vachet, J. Piven, M. Styner, G. Gerig. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 907--910. April, 2016.
DOI: 10.1109/isbi.2016.7493412

Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereas slow bulk motion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms, fast bulk motion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenuation due to the difficulty of their retrospective correction, with the disadvantage to lose full gradients for further processing. Nonetheless, such attenuation might only affect limited number of slices within a gradient volume. Q-space resampling has recently been proposed to recover corrupted slices while saving gradients for subsequent reconstruction. However, few corrupted gradients are implicitly assumed which might not hold in case of scanning unsedated infants or patients in pain. In this paper, we propose to adopt recent advances in compressive sensing based reconstruction of the diffusion orientation distribution functions (ODF) with under sampled measurements to resample corrupted slices. We make use of Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) basis functions which can analytically model ODF from arbitrary sampled signals. We demonstrate the impact of the proposed resampling strategy compared to state-of-art resampling and gradient exclusion on simulated intra-gradient motion as well as samples from real DWI data.



Kernel Partial Least Squares Regression for Relating Functional Brain Network Topology to Clinical Measures of Behavior
E. Wong, S. Palande, Bei Wang, B. Zielinski, J. Anderson, P. T. Fletcher. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2016.
DOI: 10.1109/isbi.2016.7493506

In this paper we present a novel method for analyzing the relationship between functional brain networks and behavioral phenotypes. Drawing from topological data analysis, we first extract topological features using persistent homology from functional brain networks that are derived from correlations in resting-state fMRI. Rather than fixing a discrete network topology by thresholding the connectivity matrix, these topological features capture the network organization across all continuous threshold values. We then propose to use a kernel partial least squares (kPLS) regression to statistically quantify the relationship between these topological features and behavior measures. The kPLS also provides an elegant way to combine multiple image features by using linear combinations of multiple kernels. In our experiments we test the ability of our proposed brain network analysis to predict autism severity from rs-fMRI. We show that combining correlations with topological features gives better prediction of autism severity than using correlations alone.



Bayesian Covariate Selection in Mixed Effects Models for Longitudinal Shape Analysis
P. Muralidharan, J. Fishbaugh, E. Y. Kim, H. J. Johnson, J. S. Paulsen, G. Gerig, P. T. Fletcher. In International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2016.
DOI: 10.1109/isbi.2016.7493352

The goal of longitudinal shape analysis is to understand how anatomical shape changes over time, in response to biological processes, including growth, aging, or disease. In many imaging studies, it is also critical to understand how these shape changes are affected by other factors, such as sex, disease diagnosis, IQ, etc. Current approaches to longitudinal shape analysis have focused on modeling age-related shape changes, but have not included the ability to handle covariates. In this paper, we present a novel Bayesian mixed-effects shape model that incorporates simultaneous relationships between longitudinal shape data and multiple predictors or covariates to the model. Moreover, we place an Automatic Relevance Determination (ARD) prior on the parameters, that lets us automatically select which covariates are most relevant to the model based on observed data. We evaluate our proposed model and inference procedure on a longitudinal study of Huntington's disease from PREDICT-HD. We first show the utility of the ARD prior for model selection in a univariate modeling of striatal volume, and next we apply the full high-dimensional longitudinal shape model to putamen shapes.



Disjunctive normal shape models
N. Ramesh, F. Mesadi, M. Cetin, T. Tasdizen. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2015.
DOI: 10.1109/isbi.2015.7164170

A novel implicit parametric shape model is proposed for segmentation and analysis of medical images. Functions representing the shape of an object can be approximated as a union of N polytopes. Each polytope is obtained by the intersection of M half-spaces. The shape function can be approximated as a disjunction of conjunctions, using the disjunctive normal form. The shape model is initialized using seed points defined by the user. We define a cost function based on the Chan-Vese energy functional. The model is differentiable, hence, gradient based optimization algorithms are used to find the model parameters.



A joint classification and segmentation approach for dendritic spine segmentation in 2-photon microscopy images
E. Erdil, A.O. Argunsah, T. Tasdizen, D. Unay, M. Cetin. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2015.
DOI: 10.1109/isbi.2015.7163992

Shape priors have been successfully used in challenging biomedical imaging problems. However when the shape distribution involves multiple shape classes, leading to a multimodal shape density, effective use of shape priors in segmentation becomes more challenging. In such scenarios, knowing the class of the shape can aid the segmentation process, which is of course unknown a priori. In this paper, we propose a joint classification and segmentation approach for dendritic spine segmentation which infers the class of the spine during segmentation and adapts the remaining segmentation process accordingly. We evaluate our proposed approach on 2-photon microscopy images containing dendritic spines and compare its performance quantitatively to an existing approach based on nonparametric shape priors. Both visual and quantitative results demonstrate the effectiveness of our approach in dendritic spine segmentation.



Dendritic spine shape classification from two-photon microscopy images
M.U. Ghani, S.D. Kanik, A.O. Argunsah, T. Tasdizen, D. Unay, M. Cetin. In 2015 23nd Signal Processing and Communications Applications Conference (SIU), IEEE, May, 2015.
DOI: 10.1109/siu.2015.7129985

Functional properties of a neuron are coupled with its morphology, particularly the morphology of dendritic spines. Spine volume has been used as the primary morphological parameter in order the characterize the structure and function coupling. However, this reductionist approach neglects the rich shape repertoire of dendritic spines. First step to incorporate spine shape information into functional coupling is classifying main spine shapes that were proposed in the literature. Due to the lack of reliable and fully automatic tools to analyze the morphology of the spines, such analysis is often performed manually, which is a laborious and time intensive task and prone to subjectivity. In this paper we present an automated approach to extract features using basic image processing techniques, and classify spines into mushroom or stubby by applying machine learning algorithms. Out of 50 manually segmented mushroom and stubby spines, Support Vector Machine was able to classify 98% of the spines correctly.



Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation,
F. Mesadi, M. Cetin, T. Tasdizen. In Lecture Notes in Computer Science, Springer International Publishing, pp. 703--710. 2015.
ISBN: 978-3-319-24574-4
DOI: 10.1007/978-3-319-24574-4_84

The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method.



Crowdsourcing the creation of image segmentation algorithms for connectomics
I. Arganda-Carreras, S.C. Turaga, D.R. Berger, D. Ciresan, A. Giusti, L.M. Gambardella, J. Schmidhuber, D. Laptev, S. Dwivedi, J. Buhmann, T. Liu, M. Seyedhosseini, T. Tasdizen, L. Kamentsky, R. Burget, V. Uher, X. Tan, C. Sun, T.D. Pham, E. Bas, M.G. Uzunbas, A. Cardona, J. Schindelin, H.S. Seung. In Frontiers in Neuroanatomy, Vol. 9, Frontiers Media SA, Nov, 2015.
DOI: 10.3389/fnana.2015.00142

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.



Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images
C. Jones, T. Liu, N.W. Cohan, M. Ellisman, T. Tasdizen. In Journal of Neuroscience Methods, Vol. 246, Elsevier BV, pp. 13--21. May, 2015.
DOI: 10.1016/j.jneumeth.2015.03.005

Background
In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming.

New method
We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links.

Results
We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results.

Comparison with existing methods
Post-automatic correction methods have also been used in Mishchenko et al. (2010) and Haehn et al. (2014). These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as Jeong et al. (2009) and Cardona et al. (2010) and are inherently different than our method.

Conclusion
Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication.



Informative features of local field potential signals in primary visual cortex during natural image stimulation
S.M. Seyedhosseini, S. Shushruth, T. Davis, J.M. Ichida, P.A. House, B. Greger, A. Angelucci, T. Tasdizen. In Journal of Neurophysiology, Vol. 113, No. 5, American Physiological Society, pp. 1520--1532. March, 2015.
DOI: 10.1152/jn.00278.2014

The local field potential (LFP) is of growing importance in neurophysiology as a metric of network activity and as a readout signal for use in brain-machine interfaces. However, there are uncertainties regarding the kind and visual field extent of information carried by LFP signals, as well as the specific features of the LFP signal conveying such information, especially under naturalistic conditions. To address these questions, we recorded LFP responses to natural images in V1 of awake and anesthetized macaques using Utah multielectrode arrays. First, we have shown that it is possible to identify presented natural images from the LFP responses they evoke using trained Gabor wavelet (GW) models. Because GW models were devised to explain the spiking responses of V1 cells, this finding suggests that local spiking activity and LFPs (thought to reflect primarily local synaptic activity) carry similar visual information. Second, models trained on scalar metrics, such as the evoked LFP response range, provide robust image identification, supporting the informative nature of even simple LFP features. Third, image identification is robust only for the first 300 ms following image presentation, and image information is not restricted to any of the spectral bands. This suggests that the short-latency broadband LFP response carries most information during natural scene viewing. Finally, best image identification was achieved by GW models incorporating information at the scale of ∼0.5° in size and trained using four different orientations. This suggests that during natural image viewing, LFPs carry stimulus-specific information at spatial scales corresponding to few orientation columns in macaque V1.



Nonlinear Regression with Logistic Product Basis Networks
M. Sajjadi, M. Seyedhosseini,, T. Tasdizen. In IEEE Signal Processing Letters, Vol. 22, No. 8, IEEE, pp. 1011--1015. Aug, 2015.
DOI: 10.1109/lsp.2014.2380791

We introduce a novel general regression model that is based on a linear combination of a new set of non-local basis functions that forms an effective feature space. We propose a training algorithm that learns all the model parameters simultaneously and offer an initialization scheme for parameters of the basis functions. We show through several experiments that the proposed method offers better coverage for high-dimensional space compared to local Gaussian basis functions and provides competitive performance in comparison to other state-of-the-art regression methods.



Disjunctive normal random forests
M. Seyedhosseini , T. Tasdizen. In Pattern Recognition, Vol. 48, No. 3, Elsevier BV, pp. 976--983. March, 2015.
DOI: 10.1016/j.patcog.2014.08.023

We develop a novel supervised learning/classification method, called disjunctive normal random forest (DNRF). A DNRF is an ensemble of randomly trained disjunctive normal decision trees (DNDT). To construct a DNDT, we formulate each decision tree in the random forest as a disjunction of rules, which are conjunctions of Boolean functions. We then approximate this disjunction of conjunctions with a differentiable function and approach the learning process as a risk minimization problem that incorporates the classification error into a single global objective function. The minimization problem is solved using gradient descent. DNRFs are able to learn complex decision boundaries and achieve low generalization error. We present experimental results demonstrating the improved performance of DNDTs and DNRFs over conventional decision trees and random forests. We also show the superior performance of DNRFs over state-of-the-art classification methods on benchmark datasets.



Splenium development and early spoken language in human infants
M. R. Swanson, J. J. Wolff, J. T. Elison, H. Gu, H. C. Hazlett, K. Botteron, M. Styner, S. Paterson, G. Gerig, J. Constantino, S. Dager, A. Estes, C. Vachet, J. Piven. In Developmental Science, Wiley Online Library, 2015.
ISSN: 1467-7687
DOI: 10.1111/desc.12360

The association between developmental trajectories of language-related white matter fiber pathways from 6 to 24 months of age and individual differences in language production at 24 months of age was investigated. The splenium of the corpus callosum, a fiber pathway projecting through the posterior hub of the default mode network to occipital visual areas, was examined as well as pathways implicated in language function in the mature brain, including the arcuate fasciculi, uncinate fasciculi, and inferior longitudinal fasciculi. The hypothesis that the development of neural circuitry supporting domain-general orienting skills would relate to later language performance was tested in a large sample of typically developing infants. The present study included 77 infants with diffusion weighted MRI scans at 6, 12 and 24 months and language assessment at 24 months. The rate of change in splenium development varied significantly as a function of language production, such that children with greater change in fractional anisotropy (FA) from 6 to 24 months produced more words at 24 months. Contrary to findings from older children and adults, significant associations between language production and FA in the arcuate, uncinate, or left inferior longitudinal fasciculi were not observed. The current study highlights the importance of tracing brain development trajectories from infancy to fully elucidate emerging brain–behavior associations while also emphasizing the role of the splenium as a key node in the structural network that supports the acquisition of spoken language.



Evaluating Alignment of Shapes by Ensemble Visualization
M. Raj, M. Mirzargar, R. Kirby, R. Whitaker, J. Preston. In IEEE Computer Graphics and Applications, IEEE, 2015.

The visualization of variability in 3D shapes or surfaces, which is a type of ensemble uncertainty visualization for volume data, provides a means of understanding the underlying distribution for a collection or ensemble of surfaces. While ensemble visualization for surfaces is already described in the literature, we conduct an expert-based evaluation in a particular medical imaging application: the construction of atlases or templates from a population of images. In this work, we extend contour boxplots to 3D, allowing us to evaluate it against an enumeration-style visualization of the ensemble members and also other conventional visualizations used by atlas builders, namely examining the atlas image and the corresponding images/data provided as part of the construction process. We present feedback from domain experts on the efficacy of contour boxplots compared to other modalities when used as part of the atlas construction and analysis stages of their work.



Entropy-based particle correspondence for shape populations,
I. OguzI, J. Cates, M. Datar, B. Paniagua, T. Fletcher, C. Vachet, M. Styner, R. Whitaker. In International Journal of Computer Assisted Radiology and Surgery, Springer, pp. 1-12. December, 2015.

Purpose
Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies.

Methods
In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics.

Results
We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques.

Conclusions
Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.



A Mixture Model for Automatic Diffeomorphic Multi-Atlas Building
M. Zhang, H. Shao, P. T. Fletcher. In MICCAI Workshop, Springer, 2015.

Computing image atlases that are representative of a dataset
is an important first step for statistical analysis of images. Most current approaches estimate a single atlas to represent the average of a large population of images, however, a single atlas is not sufficiently expressive to capture distributions of images with multiple modes. In this paper, we present a mixture model for building diffeomorphic multi-atlases that can represent sub-populations without knowing the category of each observed data point. In our probabilistic model, we treat diffeomorphic image transformations as latent variables, and integrate them out using a Monte Carlo Expectation Maximization (MCEM) algorithm via Hamiltonian Monte Carlo (HMC) sampling. A key benefit of our model is that the mixture modeling inference procedure results in an automatic clustering of the dataset. Using 2D synthetic data generated from known parameters, we demonstrate the ability of our model to successfully recover the multi-atlas and automatically cluster the dataset. We also show the effectiveness of the proposed method in a multi-atlas estimation problem for 3D brain images.



The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery
S. Pujol, W. Wells, C. Pierpaoli, C. Brun, J. Gee, G. Cheng, B. Vemuri, O. Commowick, S. Prima, A. Stamm, M. Goubran, A. Khan, T. Peters, P. Neher, K. H. Maier-Hein, Y. Shi, A. Tristan-Vega, G. Veni, R. Whitaker, M. Styner, C.F. Westin, S. Gouttard, I. Norton, L. Chauvin, H. Mamata, G. Gerig, A. Nabavi, A. Golby,, R. Kikinis. In Journal of Neuroimaging, Wiley, August, 2015.
DOI: 10.1111/jon.12283

BACKGROUND AND PURPOSE
Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop.

METHODS
Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods.

RESULTS
The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues.

CONCLUSIONS
The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.



Performance of an Efficient Image-registration Algorithm in Processing MR Renography Data,
C.C. Conlin, J.L. Zhang, F. Rousset, C. Vachet, Y. Zhao, K.A. Morton, K. Carlston, G. Gerig, V.S. Lee. In J Magnetic Resonance Imaging, July, 2015.
DOI: 10.1002/jmri.25000

PURPOSE:
To evaluate the performance of an edge-based registration technique in correcting for respiratory motion artifacts in magnetic resonance renographic (MRR) data and to examine the efficiency of a semiautomatic software package in processing renographic data from a cohort of clinical patients.

MATERIALS AND METHODS:
The developed software incorporates an image-registration algorithm based on the generalized Hough transform of edge maps. It was used to estimate glomerular filtration rate (GFR), renal plasma flow (RPF), and mean transit time (MTT) from 36 patients who underwent free-breathing MRR at 3T using saturation-recovery turbo-FLASH. The processing time required for each patient was recorded. Renal parameter estimates and model-fitting residues from the software were compared to those from a previously reported technique. Interreader variability in the software was quantified by the standard deviation of parameter estimates among three readers. GFR estimates from our software were also compared to a reference standard from nuclear medicine.

RESULTS:
The time taken to process one patient's data with the software averaged 12 ± 4 minutes. The applied image registration effectively reduced motion artifacts in dynamic images by providing renal tracer-retention curves with significantly smaller fitting residues (P < 0.01) than unregistered data or data registered by the previously reported technique. Interreader variability was less than 10% for all parameters. GFR estimates from the proposed method showed greater concordance with reference values (P < 0.05).

CONCLUSION:
These results suggest that the proposed software can process MRR data efficiently and accurately. Its incorporated registration technique based on the generalized Hough transform effectively reduces respiratory motion artifacts in free-breathing renographic acquisitions. J. Magn. Reson. Imaging 2015.



A Kalman Filtering Perspective for Multiatlas Segmentation
Y. Gao, L. Zhu, J. Cates, R. S. MacLeod, S. Bouix,, A. Tannenbaum. In SIAM J. Imaging Sciences, Vol. 8, No. 2, pp. 1007-1029. 2015.
DOI: 10.1137/130933423

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity—neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.