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Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

The hippocampal formation is a complex, heterogeneous structure that consists of a number of distinct, interacting subregions. Atrophy of these subregions is implied in a variety of neurodegenerative diseases, most prominently in Alzheimer's disease (AD). Thanks to the increasing resolution of MR images and computational atlases, automatic segmentation of hippocampal subregions is becoming feasible in MRI scans. Here we introduce a generative model for dedicated longitudinal segmentation that relies on subject-specific atlases. The segmentations of the scans at the different time points are jointly computed using Bayesian inference. All time points are treated the same to avoid processing bias. We evaluate this approach using over 4700 scans from two publicly available datasets (ADNI and MIRIAD). In test-retest reliability experiments, the proposed method yielded significantly lower volume differences and significantly higher Dice overlaps than the cross-sectional approach for nearly every subregion (average across subregions: 4.5% vs. 6.5%, Dice overlap: 81.8% vs. 75.4%). The longitudinal algorithm also demonstrated increased sensitivity to group differences: in MIRIAD (69 subjects: 46 with AD and 23 controls), it found differences in atrophy rates between AD and controls that the cross sectional method could not detect in a number of subregions: right parasubiculum, left and right presubiculum, right subiculum, left dentate gyrus, left CA4, left HATA and right tail. In ADNI (836 subjects: 369 with AD, 215 with early cognitive impairment - eMCI - and 252 controls), all methods found significant differences between AD and controls, but the proposed longitudinal algorithm detected differences between controls and eMCI and differences between eMCI and AD that the cross sectional method could not find: left presubiculum, right subiculum, left and right parasubiculum, left and right HATA. Moreover, many of the differences that the cross-sectional method already found were detected with higher significance. The presented algorithm will be made available as part of the open-source neuroimaging package FreeSurfer., Competing Interests: In addition, BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. BF’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Evidence suggests a link between the presence of subjective memory complaints (SMC) and lower volume of the hippocampus, one of the first regions to show neuropathological lesions in Alzheimer's disease. However, it remains unknown whether this pattern of hippocampal atrophy is regionally specific and whether SMC are also paralleled by changes in peripheral levels of amyloid-beta (Aβ)., Methods: The volume of hippocampal subregions and plasma Aβ levels were cross-sectionally compared between elderly individuals with (SMC(+); N = 47) and without SMC (SMC(-); N = 48). Significant volume differences in hippocampal subregions were further correlated with plasma Aβ levels and with objective memory performance., Results: Individuals with SMC exhibited significantly higher Aβ1-42 concentrations and lower volumes of CA1, CA4, dentate gyrus, and molecular layer compared with SMC(-) participants. Regression analyses further showed significant associations between lower volume of the dentate gyrus and both poorer memory performance and higher plasma Aβ1-42 levels in SMC(+) participants., Conclusions: The presence of SMC, lower volumes of specific hippocampal regions, and higher plasma Aβ1-42 levels could be conditions associated with aging vulnerability. If such associations are confirmed in longitudinal studies, the combination may be markers recommending clinical follow-up in nondemented older adults., (© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The human hippocampal formation is a crucial brain structure for memory and cognitive function that is closely related to other subcortical and cortical brain regions. Recent neuroimaging studies have revealed differences along the hippocampal longitudinal axis in terms of structure, connectivity, and function, stressing the importance of improving the reliability of the available segmentation methods that are typically used to divide the hippocampus into its anterior and posterior parts. However, current segmentation conventions present two main sources of variability related to manual operations intended to correct in-scanner head position across subjects and the selection of dividing planes along the longitudinal axis. Here, our aim was twofold: (1) to characterize inter- and intra-rater variability associated with these manual operations and compare manual (landmark based) and automatic (percentage based) hippocampal anterior-posterior segmentation procedures; and (2) to propose and test automated rotation methods based on approximating the hippocampal longitudinal axis to a straight line (estimated with principal component analysis, PCA) or a quadratic Bézier curve (fitted with numerical methods); as well as an automated anterior-posterior hippocampal segmentation procedure based on the percentage-based method. Our results reveal that automated rotation and segmentation procedures, used in combination or independently, minimize inconsistencies generated by the accumulation of manual operations while providing higher statistical power to detect well-known effects. A Matlab-based implementation of these procedures is made publicly available to the research community. Hum Brain Mapp 37:3353-3367, 2016. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Aligning a pair of images in a mid-space is a common approach to ensuring that deformable image registration is symmetric - that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the choice of the mid-space. In particular, the set of possible solutions is typically affected by the constraints that are enforced on the two transformations (that deform the two images), which are to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed to define the mid-space for pairwise registration. In this work, we show that by aligning the atlas to each image in the native image space, implicit-atlas-based pairwise registration can be made independent of the mid-space, thereby eliminating the need for anti-drift constraints. We derive a new symmetric cost function that only depends on a single transformation morphing one image to the other, and validate it through diffeomorphic registration experiments on brain magnetic resonance images.

Subcortical gray matter regions have been implicated in mood disorders, including Major Depressive Disorder (MDD) and Bipolar Disorder (BD). It is unclear, however, whether or how these regions differ among mood disorders and whether such abnormalities are state- or trait-like. In this study, we examined differences in subcortical gray matter volumes among euthymic BD, MDD, remitted MDD (RMD), and healthy (CTL) individuals. Using automated gray matter segmentation of T1-weighted MRI images, we estimated volumes of 16 major subcortical gray matter structures in 40 BD, 57 MDD, 35 RMD, and 61 CTL individuals. We used multivariate analysis of variance to examine group differences in these structures, and support vector machines (SVMs) to assess individual-by-individual classification. Analyses yielded significant group differences for caudate (p = 0.029) and ventral diencephalon (VD) volumes (p = 0.003). For the caudate, both the BD (p = 0.004) and the MDD (p = 0.037) participants had smaller volumes than did the CTL participants. For the VD, the MDD participants had larger volumes than did the BD and CTL participants (ps < 0.005). SVM distinguished MDD from BD with 59.5% accuracy. These findings indicate that mood disorders are characterized by anomalies in subcortical gray matter volumes and that the caudate and VD contribute uniquely to differential affective pathology. Identifying abnormalities in subcortical gray matter may prove useful for the prevention, diagnosis, and treatment of mood disorders., (Copyright © 2015. Published by Elsevier Ltd.)

Objective: To conduct a literature review on sodium alendronate, focusing on osteonecrosis of the jaws, a serious potential side effect., Background: Sodium alendronate is a bisphosphonate that is widely used for the treatment of osteopenia, osteoporosis and Paget's disease. Like other bisphosphonates, it inhibits bone resorption by inactivating osteoclasts. Alendronate has evident benefits in the treatment of these diseases, but it is associated with jaw osteonecrosis, although less frequently compared with intravenous bisphosphonates. Therefore, some preventive measures should be taken to avoid this side effect., Material and Methods: We reviewed the literature regarding the pharmacological aspects, mechanism of action, indications of use and side effects of sodium alendronate, as well as the management of patients under this therapy., Conclusion: The benefits of sodium alendronate are scientifically proven, but a serious adverse effect is osteonecrosis. Therefore, it is crucial to prepare the oral cavity before bisphosphonate therapy, providing a careful dental evaluation and all needed dental treatment., (© 2013 John Wiley & Sons A/S and The Gerodontology Society. Published by John Wiley & Sons Ltd.)

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy)., (Copyright © 2015. Published by Elsevier Inc.)

In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we combined a dataset of 39 scans with already existing manual delineations of the whole brainstem and a dataset of 10 scans in which the brainstem structures were manually labeled with a protocol that was specifically designed for this study. The resulting atlas can be used in a Bayesian framework to segment the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that, when used simultaneously, the volumes of the midbrain, pons and medulla are significantly more predictive of age than the volume of the entire brainstem, estimated as their sum. The results also demonstrate that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally, we demonstrate that the proposed algorithm is able to detect differential effects of AD on the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer., (Copyright © 2015. Published by Elsevier Inc.)

Objective: An increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1-3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol., Method: MRI scans of a single healthy adult human subject were acquired both at 3 T and 7 T. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement., Results: The differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail., Conclusions: The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies., (Copyright © 2015 Elsevier Inc. All rights reserved.)

In this paper we present a novel label fusion algorithm suited for scenarios in which different manual delineation protocols with potentially disparate structures have been used to annotate the training scans (hereafter referred to as "atlases"). Such scenarios arise when atlases have missing structures, when they have been labeled with different levels of detail, or when they have been taken from different heterogeneous databases. The proposed algorithm can be used to automatically label a novel scan with any of the protocols from the training data. Further, it enables us to generate new labels that are not present in any delineation protocol by defining intersections on the underling labels. We first use probabilistic models of label fusion to generalize three popular label fusion techniques to the multi-protocol setting: majority voting, semi-locally weighted voting and STAPLE. Then, we identify some shortcomings of the generalized methods, namely the inability to produce meaningful posterior probabilities for the different labels (majority voting, semi-locally weighted voting) and to exploit the similarities between the atlases (all three methods). Finally, we propose a novel generative label fusion model that can overcome these drawbacks. We use the proposed method to combine four brain MRI datasets labeled with different protocols (with a total of 102 unique labeled structures) to produce segmentations of 148 brain regions. Using cross-validation, we show that the proposed algorithm outperforms the generalizations of majority voting, semi-locally weighted voting and STAPLE (mean Dice score 83%, vs. 77%, 80% and 79%, respectively). We also evaluated the proposed algorithm in an aging study, successfully reproducing some well-known results in cortical and subcortical structures., (Copyright © 2014. Published by Elsevier Inc.)

Background: The purpose of this study was to investigate the influence of diabetes and corticotherapy on the development of osteonecrosis of the jaws associated with sodium alendronate., Methods: Rats were allocated into 4 groups of 11 animals each, representing different treatments: (1) alendronate; (2) alendronate and corticotherapy; (3) alendronate and diabetes; and (4) control. Tooth extractions were performed in all animals, and histological analysis was performed by hematoxylin and eosin staining and immunohistochemistry using anti-bone morphogenetic protein (BMP)-4 and anti-matrix metalloproteinase (MMP)-13 antibodies., Results: On hematoxylin and eosin analysis, proportions of inflammatory infiltrate, microbial colonies, and osteonecrosis were significantly greater in the diabetes group. BMP-4 expression in connective tissue was higher in the corticosteroid group than the alendronate group. There were no significant differences between the other groups. MMP-13 expression did not differ between the groups analyzed., Conclusion: Diabetes but not corticotherapy is associated with jaw osteonecrosis in rats undergoing alendronate therapy and subjected to tooth extractions., (Copyright © 2013 Wiley Periodicals, Inc.)

Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the technique also allows one to compute informative "error bars" on the volume estimates of individual structures., (Copyright © 2013 Elsevier B.V. All rights reserved.)

In this paper we propose a method for whole brain parcellation using the type of generative parametric models typically used in tissue classification. Compared to the non-parametric, multi-atlas segmentation techniques that have become popular in recent years, our method obtains state-of-the-art segmentation performance in both cortical and subcortical structures, while retaining all the benefits of generative parametric models, including high computational speed, automatic adaptiveness to changes in image contrast when different scanner platforms and pulse sequences are used, and the ability to handle multi-contrast (vector-valued intensities) MR data. We have validated our method by comparing its segmentations to manual delineations both within and across scanner platforms and pulse sequences, and show preliminary results on multi-contrast test-retest scans, demonstrating the feasibility of the approach.

Multi-atlas techniques are commonplace in medical image segmentation due to their high performance and ease of implementation. Locally weighting the contributions from the different atlases in the label fusion process can improve the quality of the segmentation. However, how to define these weights in a principled way in inter-modality scenarios remains an open problem. Here we propose a label fusion scheme that does not require voxel intensity consistency between the atlases and the target image to segment. The method is based on a generative model of image data in which each intensity in the atlases has an associated conditional distribution of corresponding intensities in the target. The segmentation is computed using variational expectation maximization (VEM) in a Bayesian framework. The method was evaluated with a dataset of eight proton density weighted brain MRI scans with nine labeled structures of interest. The results show that the algorithm outperforms majority voting and a recently published inter-modality label fusion algorithm.

Availability of multi-modal magnetic resonance imaging (MRI) databases opens up the opportunity to synthesize different MRI contrasts without actually acquiring the images. In theory such synthetic images have the potential to reduce the amount of acquisitions to perform certain analyses. However, to what extent they can substitute real acquisitions in the respective analyses is an open question. In this study, we used a synthesis method based on patch matching to test whether synthetic images can be useful in segmentation and inter-modality cross-subject registration of brain MRI. Thirty-nine T1 scans with 36 manually labeled structures of interest were used in the registration and segmentation of eight proton density (PD) scans, for which ground truth T1 data were also available. The results show that synthesized T1 contrast can considerably enhance the quality of non-linear registration compared with using the original PD data, and it is only marginally worse than using the original T1 scans. In segmentation, the relative improvement with respect to using the PD is smaller, but still statistically significant.

Many successful segmentation algorithms are based on Bayesian models in which prior anatomical knowledge is combined with the available image information. However, these methods typically have many free parameters that are estimated to obtain point estimates only, whereas a faithful Bayesian analysis would also consider all possible alternate values these parameters may take. In this paper, we propose to incorporate the uncertainty of the free parameters in Bayesian segmentation models more accurately by using Monte Carlo sampling. We demonstrate our technique by sampling atlas warps in a recent method for hippocampal subfield segmentation, and show a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the method also yields informative "error bars" on the segmentation results for each of the individual sub-structures.

Current label fusion methods enhance multi-atlas segmentation by locally weighting the contribution of the atlases according to their similarity to the target volume after registration. However, these methods cannot handle voxel intensity inconsistencies between the atlases and the target image, which limits their application across modalities or even across MRI datasets due to differences in image contrast. Here we present a generative model for multi-atlas image segmentation, which does not rely on the intensity of the training images. Instead, we exploit the consistency of voxel intensities within regions in the target volume and their relation to the propagated labels. This is formulated in a probabilistic framework, where the most likely segmentation is obtained with variational expectation maximization (EM). The approach is demonstrated in an experiment where T 1 -weighted MRI atlases are used to segment proton-density (PD) weighted brain MRI scans, a scenario in which traditional weighting schemes cannot be used. Our method significantly improves the results provided by majority voting and STAPLE.

The maturity of registration methods, in combination with the increasing processing power of computers, has made multi-atlas segmentation methods practical. The problem of merging the deformed label maps from the atlases is known as label fusion. Even though label fusion has been well studied for intramodality scenarios, it remains relatively unexplored when the nature of the target data is multimodal or when its modality is different from that of the atlases. In this paper, we review the literature on label fusion methods and also present an extension of our previously published algorithm to the general case in which the target data are multimodal. The method is based on a generative model that exploits the consistency of voxel intensities within the target scan based on the current estimate of the segmentation. Using brain MRI scans acquired with a multiecho FLASH sequence, we compare the method with majority voting, statistical-atlas-based segmentation, the popular package FreeSurfer and an adaptive local multi-atlas segmentation method. The results show that our approach produces highly accurate segmentations (Dice 86.3% across 22 brain structures of interest), outperforming the competing methods.

Many different probabilistic tractography methods have been proposed in the literature to overcome the limitations of classical deterministic tractography: (i) lack of quantitative connectivity information; and (ii) robustness to noise, partial volume effects and selection of seed region. However, these methods rely on Monte Carlo sampling techniques that are computationally very demanding. This study presents an approximate stochastic tractography algorithm (FAST) that can be used interactively, as opposed to having to wait several minutes to obtain the output after marking a seed region. In FAST, tractography is formulated as a Markov chain that relies on a transition tensor. The tensor is designed to mimic the features of a well-known probabilistic tractography method based on a random walk model and Monte-Carlo sampling, but can also accommodate other propagation rules. Compared to the baseline algorithm, our method circumvents the sampling process and provides a deterministic solution at the expense of partially sacrificing sub-voxel accuracy. Therefore, the method is strictly speaking not stochastic, but provides a probabilistic output in the spirit of stochastic tractography methods. FAST was compared with the random walk model using real data from 10 patients in two different ways: 1. the probability maps produced by the two methods on five well-known fiber tracts were directly compared using metrics from the image registration literature; and 2. the connectivity measurements between different regions of the brain given by the two methods were compared using the correlation coefficient ρ. The results show that the connectivity measures provided by the two algorithms are well-correlated (ρ = 0.83), and so are the probability maps (normalized cross correlation 0.818 ± 0.081). The maps are also qualitatively (i.e., visually) very similar. The proposed method achieves a 60x speed-up (7 s vs. 7 min) over the Monte Carlo sampling scheme, therefore enabling interactive probabilistic tractography: the user can quickly modify the seed region if he is not satisfied with the output without having to wait on average 7 min.

Automatic whole-brain extraction from magnetic resonance images (MRI), also known as skull stripping, is a key component in most neuroimage pipelines. As the first element in the chain, its robustness is critical for the overall performance of the system. Many skull stripping methods have been proposed, but the problem is not considered to be completely solved yet. Many systems in the literature have good performance on certain datasets (mostly the datasets they were trained/tuned on), but fail to produce satisfactory results when the acquisition conditions or study populations are different. In this paper we introduce a robust, learning-based brain extraction system (ROBEX). The method combines a discriminative and a generative model to achieve the final result. The discriminative model is a Random Forest classifier trained to detect the brain boundary; the generative model is a point distribution model that ensures that the result is plausible. When a new image is presented to the system, the generative model is explored to find the contour with highest likelihood according to the discriminative model. Because the target shape is in general not perfectly represented by the generative model, the contour is refined using graph cuts to obtain the final segmentation. Both models were trained using 92 scans from a proprietary dataset but they achieve a high degree of robustness on a variety of other datasets. ROBEX was compared with six other popular, publicly available methods (BET, BSE, FreeSurfer, AFNI, BridgeBurner, and GCUT) on three publicly available datasets (IBSR, LPBA40, and OASIS, 137 scans in total) that include a wide range of acquisition hardware and a highly variable population (different age groups, healthy/diseased). The results show that ROBEX provides significantly improved performance measures for almost every method/dataset combination.

Purpose: This work describes a spatially variant mixture model constrained by a Markov random field to model high angular resolution diffusion imaging (HARDI) data. Mixture models suit HARDI well because the attenuation by diffusion is inherently a mixture. The goal is to create a general model that can be used in different applications. This study focuses on image denoising and segmentation (primarily the former)., Methods: HARDI signal attenuation data are used to train a Gaussian mixture model in which the mean vectors and covariance matrices are assumed to be independent of spatial locations, whereas the mixture weights are allowed to vary at different lattice positions. Spatial smoothness of the data is ensured by imposing a Markov random field prior on the mixture weights. The model is trained in an unsupervised fashion using the expectation maximization algorithm. The number of mixture components is determined using the minimum message length criterion from information theory. Once the model has been trained, it can be fitted to a noisy diffusion MRI volume by maximizing the posterior probability of the underlying noiseless data in a Bayesian framework, recovering a denoised version of the image. Moreover, the fitted probability maps of the mixture components can be used as features for posterior image segmentation., Results: The model-based denoising algorithm proposed here was compared on real data with three other approaches that are commonly used in the literature: Gaussian filtering, anisotropic diffusion, and Rician-adapted nonlocal means. The comparison shows that, at low signal-to-noise ratio, when these methods falter, our algorithm considerably outperforms them. When tractography is performed on the model-fitted data rather than on the noisy measurements, the quality of the output improves substantially. Finally, ventricle and caudate nucleus segmentation experiments also show the potential usefulness of the mixture probability maps for classification tasks., Conclusions: The presented spatially variant mixture model for diffusion MRI provides excellent denoising results at low signal-to-noise ratios. This makes it possible to restore data acquired with a fast (i.e., noisy) pulse sequence to acceptable noise levels. This is the case in diffusion MRI, where a large number of diffusion-weighted volumes have to be acquired under clinical time constraints.

This work addresses the challenging problem of simultaneously segmenting multiple anatomical structures in highly varied CT scans. We propose the entangled decision forest (EDF) as a new discriminative classifier which augments the state of the art decision forest, resulting in higher prediction accuracy and shortened decision time. Our main contribution is two-fold. First, we propose entangling the binary tests applied at each tree node in the forest, such that the test result can depend on the result of tests applied earlier in the same tree and at image points offset from the voxel to be classified. This is demonstrated to improve accuracy and capture long-range semantic context. Second, during training, we propose injecting randomness in a guided way, in which node feature types and parameters are randomly drawn from a learned (nonuniform) distribution. This further improves classification accuracy. We assess our probabilistic anatomy segmentation technique using a labeled database of CT image volumes of 250 different patients from various scan protocols and scanner vendors. In each volume, 12 anatomical structures have been manually segmented. The database comprises highly varied body shapes and sizes, a wide array of pathologies, scan resolutions, and diverse contrast agents. Quantitative comparisons with state of the art algorithms demonstrate both superior test accuracy and computational efficiency.

Skull stripping is the first step in many neuroimaging analyses and its success is critical to all subsequent processing. Methods exist to skull strip brain images without gross deformities, such as those affected by Alzheimer's and Huntington's disease. However, there are no techniques for extracting brains affected by diseases that significantly disturb normal anatomy. Glioblastoma multiforme (GBM) is such a disease, as afflicted individuals develop large tumors that often require surgical resection. In this paper, we extend the ROBEX skull stripping method to extract brains from GBM images. The proposed method uses a shape model trained on healthy brains to be relatively insensitive to lesions inside the brain. The brain boundary is then searched for potential resection cavities using adaptive thresholding and the Random Walker algorithm corrects for leakage into the ventricles. The results show significant improvement over three popular skull stripping algorithms (BET, BSE and HWA) in a dataset of 48 GBM cases.

This paper presents a new supervised learning framework for the efficient recognition and segmentation of anatomical structures in 3D computed tomography (CT), with as little training data as possible. Training supervised classifiers to recognize organs within CT scans requires a large number of manually delineated exemplar 3D images, which are very expensive to obtain. In this study, we borrow ideas from the field of active learning to optimally select a minimum subset of such images that yields accurate anatomy segmentation. The main contribution of this work is in designing a combined generative-discriminative model which: i) drives optimal selection of training data; and ii) increases segmentation accuracy. The optimal training set is constructed by finding unlabeled scans which maximize the disagreement between our two complementary probabilistic models, as measured by a modified version of the Jensen-Shannon divergence. Our algorithm is assessed on a database of 196 labeled clinical CT scans with high variability in resolution, anatomy, pathologies, etc. Quantitative evaluation shows that, compared with randomly selecting the scans to annotate, our method decreases the number of training images by up to 45%. Moreover, our generative model of body shape substantially increases segmentation accuracy when compared to either using the discriminative model alone or a generic smoothness prior (e.g. via a Markov Random Field).

In this study, we present a system for Alzheimer's disease classification on the ADNI dataset. Our system is able to learn/fuse registration-based (matching) and overlap-based similarity measures, which are enhanced using a self-smoothing operator (SSO). From a matrix of pair-wise affinities between data points, our system uses a diffusion process to output an enhanced matrix. The diffusion propagates the affinity mass along the intrinsic data space without the need to explicitly learn the manifold. Using the enhanced metric in nearest neighborhood classification, we show significantly improved accuracy for Alzheimer's Disease over Diffusion Maps and a popular metric learning approach. State-of-the-art results are obtained in the classification of 120 brain MRIs from ADNI as normal, mild cognitive impairment, and Alzheimer's.

From the image analysis perspective, a disadvantage of MRI is the lack of image intensity standardization. Differences in coil sensitivity, pulse sequence and acquisition parameters lead to very different mappings from tissue properties to image intensity levels. This presents challenges for image analysis techniques because the distribution of image intensities for different brain regions can change substantially from scan to scan. Though intensity correction can sometimes alleviate this problem, it fails in more difficult scenarios in which different types of tissue are mapped to similar gray levels in one scan but different intensities in another. Here, we propose using multi-spectral data to create synthetic MRI scans matched to the intensity distribution of a given dataset using a physical model of acquisition. If the multi-spectral data are manually annotated, the labels can be transfered to the synthetic scans to build a dataset-tailored gold standard. The approach was tested on a multi-atlas based hippocampus segmentation framework using a publicly available database, significantly improving the results obtained with other intensity correction methods.

Learning-based approaches have become increasingly practical in medical imaging. For a supervised learning strategy, the quality of the trained algorithm (usually a classifier) is heavily dependent on the amount, as well as quality, of the available training data. It is often very time-consuming to obtain the ground truth manual delineations. In this paper, we propose a semi-supervised learning algorithm and show its application to skull stripping in brain MRI. The resulting method takes advantage of existing state-of-the-art systems, such as BET and FreeSurfer, to sample unlabeled data in an agreement-based framework. Using just two labeled and a set of unlabeled MRI scans, a voxel-based random forest classifier is trained to perform the skull stripping. Our system is practical, and it displays significant improvement over supervised approaches, BET and FreeSurfer in two datasets (60 test images).

A patient's electronic medical record can consist of a large number of reports, especially for an elderly patient or for one affected by a chronic disease. It can thus be cumbersome for a physician to go through all of the reports to understand the patient's complete medical history. This paper describes work in progress towards tracking medications and their dosages through the course of a patient's medical history. 923 reports associated with 11 patients were obtained from a university hospital. Drug names were identified using a dictionary look-up approach. Dosages corresponding to these drugs were determined using regular expressions. The state of a drug (ON, OFF), which determines whether or not the drug was being taken, was identified using a support vector machine with features based on expert knowledge. Results were promising: prec. approximately recall approximately 87%. The output is a timeline display of the drugs which the patient has been taking.

Automated analysis of mammograms requires robust methods for pectoralis segmentation and nipple detection. Locating the nipple is especially important in multiview computer aided detection systems, in which findings are matched across images using the nipple-to-finding distance. Segmenting the pectoralis is a key preprocessing step to avoid false positives when detecting masses due to the similarity of the texture of mammographic parenchyma and the pectoral muscle. A multiatlas algorithm capable of providing very robust initial estimates of the nipple position and pectoral region in digitized mammograms is presented here. Ten full-field digital mammograms, which are easily annotated attributed to their excellent contrast, are robustly registered to the target digitized film-screen mammogram. The annotations are then propagated and fused into a final nipple position and pectoralis segmentation. Compared to other nipple detection methods in the literature, the system proposed here has the advantages that it is more robust and can provide a reliable estimate when the nipple is located outside the image. Our results show that the change in the correlation between nipple-to-finding distances in craniocaudal and mediolateral oblique views is not significant when the detected nipple positions replace the manual annotations. Moreover, the pectoralis segmentation is acceptable and can be used as initialization for a more complex algorithm to optimize the outline locally. A novel aspect of the method is that it is also capable of detecting and segmenting the pectoralis in craniocaudal views.

Rationale and Objectives: Manual annotation of the full contour of the vertebrae in lateral x-rays for subsequent morphometry is time-consuming. The standard six-point morphometry is commonly used instead. It has been shown that the information from the complete contour improves the quality of the study. In this article, the six landmarks are given and the vertebrae are segmented taking advantage of that information. The result is a semiautomatic system in which the full contour is found with high precision, and that only requires a radiologist to mark six points per vertebra., Materials and Methods: A shape model was built for both the six landmarks and the full contours of the vertebrae L1, L2, L3, and L4 of 142 patients. The distribution of the principal components of the full contour was then modeled as a Gaussian conditional distribution depending on the principal components of the six landmarks. The conditional mean was used as initialization for active shape model optimization, and the conditional variance was used to constrain the solution to plausible shapes., Results: The achieved point-to-line error was 0.48 mm, and 95% of the points were located within 1.36 mm of the annotated contour. The accuracy is superior to those of previously published studies, at the expense of requiring the six points to be marked. Fractures and osteophytes are well approximated by the model, although they are sometimes oversmoothed., Conclusions: The proposed method provides hence a richer description than the six points, and can be used as input for shape-based morphometry to detect and quantify vertebral deformation more accurately.

Principal Component Analysis (PCA) has been widely used for dimensionality reduction in shape and appearance modeling. There have been several attempts of making PCA robust against outliers. However, there are cases in which a small subset of samples may appear as outliers and still correspond to plausible data. The example of shapes corresponding to fractures when building a vertebra shape model is addressed in this study. In this case, the modeling of "outliers" is important, and it might be desirable not only not to disregard them, but even to enhance their importance. A variation on PCA that deals naturally with the importance of outliers is presented in this paper. The technique is utilized for building a shape model of a vertebra, aiming at segmenting the spine out of lateral X-ray images. The results show that the algorithm can implement both an outlier-enhancing and a robust PCA. The former improves the segmentation performance in fractured vertebrae, while the latter does so in the unfractured ones.

The different close-packed polytypes MX and MX2 have been enumerated for each of the possible space groups by counting the corresponding Zhdanov symbols for each space group and period of stacking, P, by the use of elementary combinatorial techniques. In special cases, simple closed formulae are obtained for these numbers as functions of P. The symmetry properties of the Zhdanov symbol have been investigated with the help of its cyclotomic representation and the two-color symmetry point group thereof. Zhdanov-like rules have been developed for MX2 polytypes. The SiC cases have been generated to P = 18 under the ;1-exclusion' rule and the possible diamond polytypes have been examined.

The relationship between the space-group symmetry of a close packing of equal balls of repeat period P and the symmetry properties of its representing Zhdanov symbol is analyzed. Proofs are straightforward when some symmetry is assumed for the stacking, and it is investigated how this symmetry is reflected in the structure of the Zhdanov symbol. Most of these proofs are documented in the literature, with variable degrees of rigor. However, the proof is somewhat more involved when working backwards, i.e. when some symmetry properties for the Zhdanov symbol are assumed and the corresponding effect on the symmetry of the polytype structure it represents is investigated, which may explain why these proofs are avoided or shrugged off as ;easily seen', 'obvious' and the like.

Non-syndromic cleft lip with or without cleft palate (CL/P, MIM 119530) is among the most common of major birth defects. Homozygosity for a nonsense mutation of PVRL1, W185X, results in an autosomal recessive CL/P syndrome on Margarita Island, CLPED1 (ref. 1). Here we demonstrate highly significant association between heterozygosity for this mutation and sporadic, non-syndromic CL/P in northern Venezuela.