Slide 1
An Anatomically Curated Fiber Clustering White Matter Atlas for Consistent White Matter Tract Parcellation across the Lifespan
Slide 2
An Immersive Virtual Reality Environment for Diagnostic Imaging
Slide 3
Inter-site and Inter-scanner Diffusion MRI Data Harmonization
Slide 4
The Open Anatomy Browser: A Collaborative Web-Based Viewer for Interoperable Anatomy Atlases
Slide 5
Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort
Slide 6
Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability
Slide 7
Supra-Threshold Fiber Cluster Statistics for Data-Driven Whole Brain Tractography Analysis
Slide 8
Free Water Modeling of Peritumoral Edema using Multi-fiber Tractography
Slide 8
Estimation of Bounded and Unbounded Trajectories in Diffusion MRI
Slide 9
Principal Gradient of Macroscale Cortical Organization
Slide 10
Evolution of a Simultaneous Segmentation and Atlas Registration
Slide 11
Multi-modality MRI-based Atlas of the Brain
Slide 12
Intracranial Fluid Redistribution
Slide 13
Corticospinal Tract Modeling for Neurosurgical Planning by Tracking through Regions of Peritumoral Edema and Crossing Fibers
Slide 14
Automated White Matter Fiber Tract Identification in Patients with Brain Tumors
Slide 15
State-space Models of Mental Processes from fMRI
Slide 16
Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach
Slide 17
Tractography-driven Groupwise Multi-Scale Parcellation of the Cortex
Slide 18
Gray Matter Alterations in Early Aging
Slide 19
Statistical Shape Analysis: From Landmarks to Diffeomorphisms
Slide 20
A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation
Slide 21
Joint Modeling of Imaging and Genetic Variability
Slide 22
MR-Ultrasound Fusion for Neurosurgery
Slide 23
Diffusion MRI and Tumor Heterogeneity
Slide 24
SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research

Neuroimage Analysis Center


The Neuroimaging Analysis Center is a research and technology center with the mission of advancing the role of neuroimaging in health care. The ability to access huge cohorts of patient medical records and radiology data, the emergence of ever-more detailed imaging modalities, and the availability of unprecedented computer processing power marks the possibility for a new era in neuroimaging, disease understanding, and patient treatment. We are excited to present a national resource center with the goal of finding new ways of extracting disease characteristics from advanced imaging and computation, and to make these methods available to the larger medical community through a proven methodology of world-class research, open-source software, and extensive collaboration.

Our Sponsor



The NAC is a Biomedical Technology Resource Center supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) (P41 EB015902). It was supported by the National Center for Research Resources (NCRR) (P41 RR13218) through December 2011.

Contact the Center Directors



Carl-Fredrik Westin, PhD
Laboratory of Mathematics in Imaging
Brigham and Women's Hospital
1249 Boylston St., Room 240
Boston, MA 02215
Phone: +1 617 525-6209
E-mail: westin at

Ron Kikinis

Ron Kikinis, MD
Surgical Planning Laboratory 
Brigham and Women's Hospital 
75 Francis St, L1 Room 050
Boston, MA 02115
Phone: +1 617 732-7389
E-mail: kikinis at


Recent Publications

  • Hoyte L, Jakab M, Warfield SK, Shott S, Flesh G, Fielding JR. Levator Ani Thickness Variations in Symptomatic and Asymptomatic Women using Magnetic Resonance-based 3-dimensional Color Mapping. Am J Obstet Gynecol 2004;191(3):856-61.
    OBJECTIVE: This study was undertaken to develop and test a 3-dimensional (3D) color thickness mapping technique on levator ani imaged with magnetic resonance imaging (MRI). METHODS: Supine MRI datasets from 30 women were studied: 10 asymptomatic, 10 with urodynamic stress incontinence, and 10 with pelvic organ prolapse. Levators were manually outlined, and thickness mapping applied. Three-dimensional models were colored topographically, reflecting levator thickness. Thickness and occurrences of absent levator substance (gaps) were compared across the 3 groups, using nonparametric statistical tests. RESULTS: Color thickness mapping was successful in all subjects. There were statistically significant differences in thickness and gap percentages among the 3 groups of women, with thicker, bulkier levators in asymptomatic women, compared with women with prolapse or urodynamic stress incontinence. CONCLUSION: Color thickness mapping is feasible. It may be used to compare levators in symptomatic and asymptomatic women, to study relationships between levator thickness and pelvic floor dysfunction. This technique can be used in larger studies for hypothesis testing.
  • Park H-J, Westin C-F, Kubicki M, Maier SE, Niznikiewicz M, Baer A, Frumin M, Kikinis R, Jolesz FA, McCarley RW, Shenton ME. White matter hemisphere asymmetries in healthy subjects and in schizophrenia: a diffusion tensor MRI study. Neuroimage 2004;23(1):213-23.
    Hemisphere asymmetry was explored in normal healthy subjects and in patients with schizophrenia using a novel voxel-based tensor analysis applied to fractional anisotropy (FA) of the diffusion tensor. Our voxel-based approach, which requires precise spatial normalization to remove the misalignment of fiber tracts, includes generating a symmetrical group average template of the diffusion tensor by applying nonlinear elastic warping of the demons algorithm. We then normalized all 32 diffusion tensor MRIs from healthy subjects and 23 from schizophrenic subjects to the symmetrical average template. For each brain, six channels of tensor component images and one T2-weighted image were used for registration to match tensor orientation and shape between images. A statistical evaluation of white matter asymmetry was then conducted on the normalized FA images and their flipped images. In controls, we found left-higher-than-right anisotropic asymmetry in the anterior part of the corpus callosum, cingulum bundle, the optic radiation, and the superior cerebellar peduncle, and right-higher-than-left anisotropic asymmetry in the anterior limb of the internal capsule and the anterior limb’s prefrontal regions, in the uncinate fasciculus, and in the superior longitudinal fasciculus. In patients, the asymmetry was lower, although still present, in the cingulum bundle and the anterior corpus callosum, and not found in the anterior limb of the internal capsule, the uncinate fasciculus, and the superior cerebellar peduncle compared to healthy subjects. These findings of anisotropic asymmetry pattern differences between healthy controls and patients with schizophrenia are likely related to neurodevelopmental abnormalities in schizophrenia.
  • Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the relevant clinical imaging data. However, the most appropriate measure or set of measures with which to compare such segmentations has not been clarified and several measures are used in practice. We present here an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE). The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation. The source of each segmentation in the collection may be an appropriately trained human rater or raters, or may be an automated segmentation algorithm. The probabilistic estimate of the true segmentation is formed by estimating an optimal combination of the segmentations, weighting each segmentation depending upon the estimated performance level, and incorporating a prior model for the spatial distribution of structures being segmented as well as spatial homogeneity constraints. STAPLE is straightforward to apply to clinical imaging data, it readily enables assessment of the performance of an automated image segmentation algorithm, and enables direct comparison of human rater and algorithm performance.
  • Pichon E, Tannenbaum A, Kikinis R. A statistically based flow for image segmentation. Med Image Anal 2004;8(3):267-74.
    In this paper we present a new algorithm for 3D medical image segmentation. The algorithm is versatile, fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise.
  • Kozinska D, Holland CM, Krissian K, Westin C-F, Guttmann CRG. A method for the analysis of the geometrical relationship between white matter pathology and the vascular architecture of the brain. Neuroimage 2004;22(4):1671-8.
    A novel method for the visual and quantitative analysis of the geometrical relationship between the vascular architecture of the brain and white matter pathology is presented. The cerebro vascular system is implicated in the pathogenesis of many diseases of the cerebral white matter, for example, stroke, microcerebrovascular disease, and multiple sclerosis (MS). In our work, white matter lesions and vessels are depicted using magnetic resonance imaging (MRI) and extracted using image analysis techniques. We focus on measuring distance relationships between white matter lesions and vessels, and distribution of lesions with respect to vessel caliber. Vascular distance maps are generated by computing for each voxel the Euclidean distance to the closest vessel. Analogously, radius maps assign the radius of the closest vessel to each voxel in the image volume. The distance and radius maps are used to analyze the distribution of lesions with respect to the vessels’ locations and their calibers. The method was applied to three MS patients to demonstrate its functionality and feasibility. Preliminary findings indicate that larger MS lesions tend to be farther from detected vessels and that the caliber of the vessels nearest to larger lesions tends to be smaller, suggesting a possible role of relative hypoperfusion or hypoxia in lesion formation.