We describe a neuro imaging protocol that utilizes an anatomical atlas of the human head to guide Diffuse optical tomography of human brain activation. The protocol is demonstrated by imaging the hemodynamic response to median nerve stimulation in three healthy subjects, and comparing the images obtained using a head atlas with the images obtained using the subject-specific head anatomy. The results indicate that using the head atlas anatomy it is possible to reconstruct the location of the brain activation to the expected gyrus of the brain, in agreement with the results obtained with the subject-specific head anatomy. The benefits of this novel method derive from eliminating the need for subject-specific head anatomy and thus obviating the need for a subject-specific MRI to improve the anatomical interpretation of Diffuse optical tomography images of brain activation.
Diffuse Optical Tomography; NIRS; anatomical atlas; MRI; segmentation; registration; inverse problem; human study; somatosensory; PACS: 87.19.lf; PACS: 87.19.lh
A World Wide Web Common Gateway Interface package is described for accessing existing online interactive atlases of anatomy. The Web interface accesses the same 2-D and 3-D images of human neuroanatomy, knee anatomy and thoracic viscera that are currently accessed by a custom interactive atlas in distance learning courses. Although the Web interface is too slow to replace the existing atlas, it provides a parallel access path that has much broader potential for development of a distributed distance learning network in anatomy. By maintaining both access methods to the same information sources we continue to satisfy the fast interactivity needs for our local courses, while at the same time providing a migration path to the Web as the capabilities of Web browsers evolve.
Conflicts can occur between the principle of freedom of information treasured by librarians and ethical standards of scientific research involving the propriety of using data derived from immoral or dishonorable experimentation. A prime example of this conflict was brought to the attention of the medical and library communities in 1995 when articles claiming that the subjects of the illustrations in the classic anatomy atlas, Eduard Pernkopf's Topographische Anatomie des Menschen, were victims of the Nazi holocaust. While few have disputed the accuracy, artistic, or educational value of the Pernkopf atlas, some have argued that the use of such subjects violates standards of medical ethics involving inhuman and degrading treatment of subjects or disrespect of a human corpse. Efforts were made to remove the book from medical libraries. In this article, the history of the Pernkopf atlas and the controversy surrounding it are reviewed. The results of a survey of academic medical libraries concerning their treatment of the Pernkopf atlas are reported, and the ethical implications of these issues as they affect the responsibilities of librarians is discussed.
Structural delineation and assignment are the fundamental steps in understanding the anatomy of the human brain. The white matter has been structurally defined in the past only at its core regions (deep white matter). However, the most peripheral white matter areas, which are interleaved between the cortex and the deep white matter, have lacked clear anatomical definitions and parcellations. We used axonal fiber alignment information from diffusion tensor imaging (DTI) to delineate the peripheral white matter, and investigated its relationship with the cortex and the deep white matter. Using DTI data from 81 healthy subjects, we identified nine common, blade-like anatomical regions, which were further parcellated into 21 subregions based on the cortical anatomy. Four short association fiber tracts connecting adjacent gyri (U-fibers) were also identified reproducibly among the healthy population. We anticipate that this atlas will be useful resource for atlas-based white matter anatomical studies.
human; white matter; atlas; association fiber; magnetic resonance imaging; diffusion tensor
In humans, congenital heart diseases are common. Since the rapid progression of transgenic technologies, the mouse has become the major animal model of defective cardiovascular development. Moreover, genetically modified mice frequently die in utero, commonly due to abnormal cardiovascular development. A variety of publications address specific developmental stages or structures of the mouse heart, but a single reference reviewing and describing the anatomy and histology of cardiac developmental events, stage by stage, has not been available. The aim of this color atlas, which demonstrates embryonic/fetal heart development, is to provide a tool for pathologists and biomedical scientists to use for detailed histological evaluation of hematoxylin and eosin (H&E)-stained sections of the developing mouse heart with emphasis on embryonic days (E) 11.5–18.5. The selected images illustrate the main structures and developmental events at each stage and serve as reference material for the confirmation of the chronological age of the embryo/early fetus and assist in the identification of any abnormalities. An extensive review of the literature covering cardiac development pre-E11.5 is summarized in the introduction. Although the focus of this atlas is on the descriptive anatomic and histological development of the normal mouse heart from E11.5 to E18.5, potential embryonic cardiac lesions are discussed with a list of the most common transgenic pre- and perinatal heart defects. Representative images of hearts at E11.5-15.5 and E18.5 are provided in Figures 2-4, 6, 8, and 9. A complete set of labeled images (Figures E11.5-18.5) is available on the CD enclosed in this issue of Toxicologic Pathology. All digital images can be viewed online at https://niehsimages.epl-inc.com with the username “ToxPath” and the password “embryohearts.”
heart; embryo; mouse; in utero lethality
Currently no high resolution three-dimensional (3-D) digital atlas exists that is optimized for normal human adult anatomy. We have developed the methods for defining, constructing and evaluating a “minimal deformation target” MDT brain for multi-subject studies, which produces a single reproducible target based on the common features of a group of brain images. We are building a digital brain atlas, based on a high-resolution, high contrast MR study — CH brain. In a preliminary study (N=27) MDT optimization of the CH brain resulted in a significant reduction of its individual features, including changes in the central sulcus, Sylvian fissure, ventricular system and temporal lobe regions. The atlas will be created by optimizing the CH brain to a large population (N>100) of normal subjects
The use of Java applications through applets, HTML facilities and CGI scripts provides useful interactivity to an on-line atlas of topographic anatomy via Internet, based on the Visible Human Project.
This paper describes the SRI24 atlas, a new standard reference system of normal human brain anatomy, that was created using template-free population registration of high-resolution magnetic resonance images acquired at 3T in a group of 24 normal control subjects. The atlas comprises anatomical channels (T1, T2, and proton density weighted), diffusion-related channels (fractional anisotropy, mean diffusivity, longitudinal diffusivity, mean diffusion-weighted image), tissue channels (CSF probability, gray matter probability, white matter probability, tissue labels), and two cortical parcellation maps. The SRI24 atlas enables multi-channel atlas-to-subject image registration. It is uniquely versatile in that it is equally suited for the two fundamentally different atlas applications: label propagation and spatial normalization. Label propagation, herein demonstrated using DTI fiber tracking, is enabled by the increased sharpness of the SRI24 atlas compared with other available atlases. Spatial normalization, herein demonstrated using data from a young-old group comparison study, is enabled by its unbiased average population shape property. For both propagation and normalization, we also report the results of quantitative comparisons with seven other published atlases: Colin27, MNI152, ICBM452 (warp5 and air12), and LPBA40 (SPM5, FLIRT, AIR). Our results suggest that the SRI24 atlas, although based on 3T MR data, allows equally accurate spatial normalization of data acquired at 1.5T as the comparison atlases, all of which are based on 1.5T data. Furthermore, the SRI24 atlas is as suitable for label propagation as the comparison atlases and detailed enough to allow delineation of anatomical structures for this purpose directly in the atlas.
brain atlas; multi-spectral magnetic resonance imaging; diffusion tensor imaging; unbiased population registration; spatial normalization; label propagation
Imaging of the human fetus using magnetic resonance (MR) is an essential tool for quantitative studies of normal as well as abnormal brain development in utero. However, because of fundamental differences in tissue types, tissue properties and tissue distribution between the fetal and adult brain, automated tissue segmentation techniques developed for adult brain anatomy are unsuitable for this data. In this paper, we describe methodology for automatic atlas-based segmentation of individual tissue types in motion-corrected 3D volumes reconstructed from clinical MR scans of the fetal brain. To generate anatomically correct automatic segmentations, we create a set of accurate manual delineations and build an in utero 3D statistical atlas of tissue distribution incorporating developing grey and white matter as well as transient tissue types such as the germinal matrix. The probabilistic atlas is associated with an unbiased average shape and intensity template for registration of new subject images to the space of the atlas. Quantitative whole brain 3D validation of tissue labeling performed on a set of 14 fetal MR scans (20.57–22.86 weeks gestational age) demonstrates that this atlas-based EM segmentation approach achieves consistently high DSC performance for the main tissue types in the fetal brain. This work indicates that reliable measures of brain development can be automatically derived from clinical MR imaging and opens up possibility of further 3D volumetric and morphometric studies with multiple fetal subjects.
Cerebral viscoelastic constants can be measured in a noninvasive, image-based way by magnetic resonance elastography (MRE) for the detection of neurological disorders. However, MRE brain maps of viscoelastic constants are still limited by low spatial resolution. Here we introduce three-dimensional multifrequency MRE of the brain combined with a novel reconstruction algorithm based on a model-free multifrequency inversion for calculating spatially resolved viscoelastic parameter maps of the human brain corresponding to the dynamic range of shear oscillations between 30 and 60 Hz. Maps of two viscoelastic parameters, the magnitude and the phase angle of the complex shear modulus, |G*| and φ, were obtained and normalized to group templates of 23 healthy volunteers in the age range of 22 to 72 years. This atlas of the anatomy of brain mechanics reveals a significant contrast in the stiffness parameter |G*| between different anatomical regions such as white matter (WM; 1.252±0.260 kPa), the corpus callosum genu (CCG; 1.104±0.280 kPa), the thalamus (TH; 1.058±0.208 kPa) and the head of the caudate nucleus (HCN; 0.649±0.101 kPa). φ, which is sensitive to the lossy behavior of the tissue, was in the order of CCG (1.011±0.172), TH (1.037±0.173), CN (0.906±0.257) and WM (0.854±0.169). The proposed method provides the first normalized maps of brain viscoelasticity with anatomical details in subcortical regions and provides useful background data for clinical applications of cerebral MRE.
To review the dose limits and standardize the three-dimenional (3D) radiographic definition for the organs at risk (OARs) for thoracic radiotherapy (RT), including the lung, proximal bronchial tree, esophagus, spinal cord, ribs, and brachial plexus.
Methods and Materials
The present study was performed by representatives from the Radiation Therapy Oncology Group, European Organization for Research and Treatment of Cancer, and Soutwestern Oncology Group lung cancer committees. The dosimetric constraints of major multicenter trials of 3D-conformal RT and stereotactic body RT were reviewed and the challenges of 3D delineation of these OARs described. Using knowledge of the human anatomy and 3D radiographic correlation, draft atlases were generated by a radiation oncologist, medical physicist, dosimetrist, and radiologist from the United States and reviewed by a radiation oncologist and medical physicist from Europe. The atlases were then critically reviewed, discussed, and edited by another 10 radiation oncologists.
Three-dimensional descriptions of the lung, proximal bronchial tree, esophagus, spinal cord, ribs, and brachial plexus are presented. Two computed tomography atlases were developed: one for the middle and lower thoracic OARs (except for the heart) and one focusing on the brachial plexus for a patient positioned supine with their arms up for thoracic RT. The dosimetric limits of the key OARs are discussed.
We believe these atlases will allow us to define OARs with less variation and generate dosimetric data in a more consistent manner. This could help us study the effect of radiation on these OARs and guide high-quality clinical trials and individualized practice in 3D-conformal RT and stereotactic body RT.
Atlas; Lung; Esophagus; Spinal cord; Brachial plexus
We describe the validation of an anatomical brain atlas approach to the analysis of diffuse optical tomography (DOT). Using MRI data from 32 subjects, we compare the diffuse optical images of simulated cortical activation reconstructed using a registered atlas with those obtained using a subject’s true anatomy. The error in localization of the simulated cortical activations when using a registered atlas is due to a combination of imperfect registration, anatomical differences between atlas and subject anatomies and the localization error associated with diffuse optical image reconstruction. When using a subject-specific MRI, any localization error is due to diffuse optical image reconstruction only. In this study we determine that using a registered anatomical brain atlas results in an average localization error of approximately 18 mm in Euclidean space. The corresponding error when the subject’s own MRI is employed is 9.1 mm. In general, the cost of using atlas-guided DOT in place of subject-specific MRI-guided DOT is a doubling of the localization error. Our results show that despite this increase in error, reasonable anatomical localization is achievable even in cases where the subject-specific anatomy is unavailable.
Diffuse optical tomography; NIRS; MRI; Anatomical atlas; Registration
Conventional automated segmentation techniques for magnetic resonance imaging (MRI) fail to perform in a robust and consistent manner when brain anatomy differs wildly from expectations – as is often the case in brain cancers. We propose a novel out-of-atlas technique to estimate the spatial extent of abnormal brain regions by combining multi-atlas based segmentation with semi-local non-parametric intensity analysis. In a study with 30 clinically-acquired MRI scans of patients with malignant gliomas and 29 atlases of normal anatomy from research acquisitions, we demonstrate that this technique robustly identifies cancerous regions. The resulting segmentations could be used to study cancer morphometrics or guide selection/application/refinement of tumor analysis models or regional image quantification approaches.
Cancer Segmentation; Tumors; Multi-Atlas Segmentation; Out-of-Atlas Labeling
Long INterspersed Element-1 (LINE-1 or L1) retrotransposons are the only autonomously active transposable elements in the human genome. The average human genome contains ∼80-100 active L1s, but only a subset of these L1s are highly active or ‘hot’. Human L1s are closely related in sequence, making it difficult to decipher progenitor/offspring relationships using traditional phylogenetic methods. However, L1 mRNAs can sometimes bypass their own polyadenylation signal and instead utilize fortuitous polyadenylation signals in 3′ flanking genomic DNA. Retrotransposition of the resultant mRNAs then results in lineage specific sequence ‘tags’ (i.e., 3′ transductions) that mark the descendants of active L1 progenitors. Here, we developed a method (Transduction-Specific Amplification Typing of L1 Active Subfamilies or TS-ATLAS) that exploits L1 3′ transductions to identify active L1 lineages in a genome-wide context. TS-ATLAS enabled the characterisation of a putative active progenitor of one L1 lineage that includes the disease causing L1 insertion L1RP, and the identification of new retrotransposition events within two other ‘hot’ L1 lineages. Intriguingly, the analysis of the newly discovered transduction lineage members suggests that L1 polyadenylation, even within a lineage, is highly stochastic. Thus, TS-ATLAS provides a new tool to explore the dynamics of L1 lineage evolution and retrotransposon biology.
human; retrotransposon; transduction; polyadenylation; genome
ATLAS-plus [Advanced Tools for Learning Anatomical Structure] is a multimedia program used to assist in the teaching of anatomy at the University of Michigan Medical School. ATLAS-plus contains three courses: Histology, Embryology, and Gross Anatomy. In addition to the three courses, a glossary containing terms from the three courses is available. All three courses and the glossary are accessible in the ATLAS-plus environment. The ATLAS-plus environment provides a consistent set of tools and options so that the user can navigate easily and intelligently in and between the various courses and modules in the ATLAS-plus world. The program is a collaboration between anatomy and cell biology faculty, medical students, graphic artists, systems analysts, and instructional designers.
One of the primary goals of computational anatomy is the statistical analysis of anatomical variability in large populations of images. The study of anatomical shape is inherently related to the construction of transformations of the underlying coordinate space, which map one anatomy to another. It is now well established that representing the geometry of shapes or images in Euclidian spaces undermines our ability to represent natural variability in populations. In our previous work we have extended classical statistical analysis techniques, such as averaging, principal components analysis, and regression, to Riemannian manifolds, which are more appropriate representations for describing anatomical variability. In this paper we extend the notion of robust estimation, a well established and powerful tool in traditional statistical analysis of Euclidian data, to manifold-valued representations of anatomical variability. In particular, we extend the geometric median, a classic robust estimator of centrality for data in Euclidean spaces. We formulate the geometric median of data on a Riemannian manifold as the minimizer of the sum of geodesic distances to the data points. We prove existence and uniqueness of the geometric median on manifolds with non-positive sectional curvature and give sufficient conditions for uniqueness on positively curved manifolds. Generalizing the Weiszfeld procedure for finding the geometric median of Euclidean data, we present an algorithm for computing the geometric median on an arbitrary manifold. We show that this algorithm converges to the unique solution when it exists. In this paper we exemplify the robustness of the estimation technique by applying the procedure to various manifolds commonly used in the analysis of medical images. Using this approach, we also present a robust brain atlas estimation technique based on the geometric median in the space of deformable images.
Robust statistics; Riemannian manifolds; Deformable Atlases; Diffeomorphisms
Although various posterior insertion angles for screw insertion have been proposed for C1 lateral mass, substantial conclusions have not been reached regarding ideal angles and average length of the screw yet. We aimed to re-consider the morphometry and the ideal trajections of the C1 screw. Morphometric analysis was performed on 40 Turkish dried atlas vertebrae obtained from the Department of Anatomy at the Medical School of Ankara University. The quantitative anatomy of the screw entry zone, trajectories, and the ideal lengths of the screws were calculated to evaluate the feasibility of posterior screw fixation of the lateral mass of the atlas. The entry point into the lateral mass of the atlas is the intersection of the posterior arch and the C1 lateral mass. The optimum medial angle is 13.5 ± 1.9° and maximal angle of medialization is 29.4 ± 3.0°. The ideal cephalic angle is 15.2 ± 2.6°, and the maximum cephalic angle is 29.6 ± 2.6°. The optimum screw length was found to be 19.59 ± 2.20 mm. With more than 30° of medial trajections and cephalic trajections the screw penetrates into the spinal canal and atlantooccipital joint, respectively. Strikingly, in 52% of our specimens, the height of the inferior articular process was under 3.5 mm, and in 70% was under 4 mm, which increases the importance of the preparation of the screw entry site. For accommodation of screws of 3.5-mm in diameter, the starting point should be taken as the insertion of the posterior arch at the superior end of the inferior articular process with a cephalic trajection. This study may aid many surgeons in their attempts to place C1 lateral mass screws.
Atlas; C-1; Entry zone; Lateral mass screw; Morphometry; Trajection
Estimation of internal mouse anatomy is required for quantitative bioluminescence or fluorescence tomography. However, only surface range data can be recovered from all-optical systems. These data are at times sparse or incomplete. We present a method for fitting an elastically deformable mouse atlas to surface topographic range data acquired by an optical system. In this method, we first match the postures of a deformable atlas and the range data of the mouse being imaged. This is achieved by aligning manually identified landmarks. We then minimize the asymmetric L2 pseudo-distance between the surface of the deformable atlas and the surface topography range data. Once this registration is accomplished, the internal anatomy of the atlas is transformed to the coordinate system of the range data using elastic energy minimization. We evaluated our method by using it to register a digital mouse atlas to a surface model produced from a manually labeled CT mouse data set. Dice coefficents indicated excellent agreement in the brain and heart, with fair agreement in the kidneys and bladder. We also present example results produced using our method to align the digital mouse atlas to surface range data.
Deformable atlas; mouse registration; optical tomography
For pre-clinical bioluminescence or fluorescence optical tomography, the animal's surface topography and internal anatomy need to be estimated for improving the quantitative accuracy of reconstructed images. The animal's surface profile can be measured by all-optical systems, but estimation of the internal anatomy using optical techniques is non-trivial. A 3D anatomical mouse atlas may be warped to the estimated surface. However, fitting an atlas to surface topography data is challenging because of variations in the posture and morphology of imaged mice. In addition, acquisition of partial data (for example, from limited views or with limited sampling) can make the warping problem ill-conditioned. Here, we present a method for fitting a deformable mouse atlas to surface topographic range data acquired by an optical system. As an initialization procedure, we match the posture of the atlas to the posture of the mouse being imaged using landmark constraints. The asymmetric L2 pseudo-distance between the atlas surface and the mouse surface is then minimized in order to register two data sets. A Laplacian prior is used to ensure smoothness of the surface warping field. Once the atlas surface is normalized to match the range data, the internal anatomy is transformed using elastic energy minimization. We present results from performance evaluation studies of our method where we have measured the volumetric overlap between the internal organs delineated directly from MRI or CT and those estimated by our proposed warping scheme. Computed Dice coefficients indicate excellent overlap in the brain and the heart, with fair agreement in the kidneys and the bladder.
We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls.
tractography; diffusion MRI; white matter
eMouseAtlas (www.emouseatlas.org) is a comprehensive online resource to visualise mouse development and investigate gene expression in the mouse embryo. We have recently deployed a completely redesigned Mouse Anatomy Atlas website (www.emouseatlas.org/emap/ema) that allows users to view 3D embryo reconstructions, delineated anatomy, and high-resolution histological sections. A new feature of the website is the IIP3D web tool that allows a user to view arbitrary sections of 3D embryo reconstructions using a web browser. This feature provides interactive access to very high-volume 3D images via a tiled pan-and-zoom style interface and circumvents the need to download large image files for visualisation. eMouseAtlas additionally includes EMAGE (Edinburgh Mouse Atlas of Gene Expression) (www.emouseatlas.org/emage), a freely available, curated online database of in situ gene expression patterns, where gene expression domains extracted from raw data images are spatially mapped into atlas embryo models. In this way, EMAGE introduces a spatial dimension to transcriptome data and allows exploration of the spatial similarity between gene expression patterns. New features of the EMAGE interface allow complex queries to be built, and users can view and compare multiple gene expression patterns. EMAGE now includes mapping of 3D gene expression domains captured using the imaging technique optical projection tomography. 3D mapping uses WlzWarp, an open-source software tool developed by eMouseAtlas.
Most diffusion imaging studies have used subject registration to an atlas space for enhanced quantification of anatomy. However, standard diffusion tensor atlases lack information in regions of fiber crossing and are based on adult anatomy. The degree of error associated with applying these atlases to studies of children for example has not yet been estimated but may lead to suboptimal results. This paper describes a novel technique for generating population-specific high angular resolution diffusion imaging (HARDI)-based atlases consisting of labeled regions of homogenous white matter. Our approach uses a fiber orientation distribution (FOD) diffusion model and a data driven clustering algorithm. White matter regional labeling is achieved by our automated data driven clustering algorithm that has the potential to delineate white matter regions based on fiber complexity and orientation. The advantage of such an atlas is that it is study specific and more comprehensive in describing regions of white matter homogeneity as compared to standard anatomical atlases. We have applied this state of the art technique to a dataset consisting of adolescent and preadolescent children, creating one of the first examples of a HARDI-based atlas, thereby establishing the feasibility of the atlas creation framework. The white matter regions generated by our automated clustering algorithm have lower FOD variance than when compared to the regions created from a standard anatomical atlas.
Diffusion; Atlas Generation; HARDI Template; White Matter Parcellation
Conformal radiotherapy planning needs accurate delineations of the critical structures. Atlas-based segmentation has been shown to be very efficient to delineate brain structures. It would therefore be very interesting to develop an atlas for the head and neck region where 7 % of the cancers arise. However, the construction of an atlas in this region is very difficult due to the high variability of the anatomies. This can generate segmentation errors and over-segmented structures in the atlas. To overcome this drawback, we present an alternative method to build a template locally adapted to the patient’s anatomy. This is done first by selecting in a database the images that are the most similar to the patient on predefined regions of interest, using on a distance between transformations. The first major contribution is that we do not compute every patient-to-image registration to find the most similar image, but only the registration of the patient towards an average image. This method is therefore computationally very efficient. The second major contribution is a novel method to use the selected images and the predefined regions to build a Frankenstein’s creature” for segmentation. We present a qualitative and quantitative comparison between the proposed method and a classical atlas-based segmentation method. This evaluation is performed on a subset of 58 patients among a database of 105 head and neck CT images and shows a great improvement of the specificity of the results.
Labeling or segmentation of structures of interest on medical images plays an essential role in both clinical and scientific understanding of the biological etiology, progression, and recurrence of pathological disorders. Here, we focus on the optic nerve, a structure that plays a critical role in many devastating pathological conditions – including glaucoma, ischemic neuropathy, optic neuritis and multiple-sclerosis. Ideally, existing fully automated procedures would result in accurate and robust segmentation of the optic nerve anatomy. However, current segmentation procedures often require manual intervention due to anatomical and imaging variability. Herein, we propose a framework for robust and fully-automated segmentation of the optic nerve anatomy. First, we provide a robust registration procedure that results in consistent registrations, despite highly varying data in terms of voxel resolution and image field-of-view. Additionally, we demonstrate the efficacy of a recently proposed non-local label fusion algorithm that accounts for small scale errors in registration correspondence. On a dataset consisting of 31 highly varying computed tomography (CT) images of the human brain, we demonstrate that the proposed framework consistently results in accurate segmentations. In particular, we show (1) that the proposed registration procedure results in robust registrations of the optic nerve anatomy, and (2) that the non-local statistical fusion algorithm significantly outperforms several of the state-of-the-art label fusion algorithms.
Multi-Atlas Segmentation; Computed Tomography; Optic Nerve; Non-Local STAPLE
EMAGE (http://genex.hgu.mrc.ac.uk/Emage/database) is a database of in situ gene expression patterns in the developing mouse embryo. Domains of expression from raw data images are spatially integrated into a set of standard 3D virtual mouse embryos at different stages of development, allowing data interrogation by spatial methods. Sites of expression are also described using an anatomy ontology and data can be queried using text-based methods. Here we describe recent enhancements to EMAGE which include advances in spatial search methods including: a refined local spatial similarity search algorithm, a method to allow global spatial comparison of patterns in EMAGE and subsequent hierarchical-clustering, and spatial searches across multiple stages of development. In addition, we have extended data access by the introduction of web services and new HTML-based search interfaces, which allow access to data that has not yet been spatially annotated. We have also started incorporating full 3D images of gene expression that have been generated using optical projection tomography (OPT).