Ali R. Khan

FreeSurfer-Initiated Fully-Automated Subcortical Brain Segmentation in MRI Using Large Deformation Diffeomorphic Metric Mapping

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM)-based label-propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse-to-fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM). One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail. We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0.81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS+LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntingtons disease and Tourettes syndrome subjects, and the right hippocampus of Schizophrenia subjects.

MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

The challenge of mapping the human connectome based on diffusion tractography

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.

Symmetric Data Attachment Terms for Large Deformation Image Registration

Nonrigid medical image registration between images that are linked by an invertible transformation is an inherently symmetric problem. The transformation that registers the image pair should ideally be the inverse of the transformation that registers the pair with the order of images interchanged. This property is referred to as symmetry in registration or inverse consistent registration. However, in practical estimation, the available registration algorithms have tended to produce inverse inconsistent transformations when the template and target images are interchanged. In this paper, we propose two novel cost functions in the large deformation diffeomorphic framework that are inverse consistent. These cost functions have symmetric data-attachment terms; in the first, the matching error is measured at all points along the flow between template and target, and in the second, matching is enforced only at the midpoint of the flow between the template and target. We have implemented these cost functions and present experimental results to validate their inverse consistent property and registration accuracy.

Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): Validation on hippocampus segmentation

We developed a novel method for spatially-local selection of atlas-weights in multi-atlas segmentation that combines supervised learning on a training set and dynamic information in the form of local registration accuracy estimates (SuperDyn). Supervised learning was applied using a jackknife learning approach and the methods were evaluated using leave-N-out cross-validation. We applied our segmentation method to hippocampal segmentation in 1.5T and 3T MRI from two datasets: 69 healthy middle-aged subjects (aged 44-49) and 37 healthy and cognitively-impaired elderly subjects (aged 72-84). Mean Dice overlap scores (left hippocampus, right hippocampus) of (83.3, 83.2) and (85.1, 85.3) from the respective datasets were found to be significantly higher than those obtained via equally-weighted fusion, STAPLE, and dynamic fusion. In addition to global surface distance and volume metrics, we also investigated accuracy at a spatially-local scale using a surface-based segmentation performance assessment method (SurfSPA), which generates cohort-specific maps of segmentation accuracy quantified by inward or outward displacement relative to the manual segmentations. These measurements indicated greater agreement with manual segmentation and lower variability for the proposed segmentation method, as compared to equally-weighted fusion.

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery

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.

Tractography-based connectomes are dominated by false-positive connections

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

Representation of time-varying shapes in the large deformation diffeomorphic framework

Tracking and representation of shape change over time is of great interest in the field of computational anatomy. We propose a longitudinal growth model which estimates the diffeomorphic flow of a baseline image passing through a series of time-points that are the observed evolution of the template over time. We optimize the full space-time flow for the sequence of images, providing a linear space representation of the shape-change via a time-dependent velocity vector field, thus application of linear techniques becomes straightforward. We test our longitudinal growth model on both synthetic and real data-sets and demonstrate flexibility in time- point spacing, generation of average growth, and robust interpolation of missing time-points.

Computing an average anatomical atlas using LDDMM and geodesic shooting

Average 3D digital atlas construction from a set of images is an important task for registration and assessment of intra- and inter-population differences in structural and functional imagery. In this paper, we describe the computation of an average atlas using LDDMM and geodesic shooting where the velocity vector fields transforming a provisional template to the ensemble are averaged and evolved via shooting using the equation for the geodesic conservation of momentum to give the average atlas. This guarantees that the averaged atlas so computed remains within the permissible shape space of the anatomical ensemble being averaged

Fully-automated, multi-stage hippocampus mapping in very mild Alzheimer disease

Landmark‐based high‐dimensional diffeomorphic maps of the hippocampus (although accurate) is highly‐dependent on raters anatomic knowledge of the hippocampus in the magnetic resonance images. It is therefore vulnerable to rater drift and errors if substantial amount of effort is not spent on quality assurance, training, and re‐training. A fully‐automated, FreeSurfer‐initialized large‐deformation diffeomorphic metric mapping procedure of small brain substructures, including the hippocampus, has been previously developed and validated in small samples. In this report, we demonstrate that this fully‐automated pipeline can be used in place of the landmark‐based procedure in a large‐sample clinical study to produce similar statistical outcomes. Some direct comparisons of the two procedures are also presented.