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PubMed1977

A forthcoming cross-sectional atlas correlating CT scans with anatomical structures.

With the development of the whole-body CAT (computerized axial tomographic) scanner, a need has arisen for a cross-sectional anatomy book that correlates acutal CT scans with detailed anatomical structures. This article describes a forthcoming cross-sectional atlas using CT scans of normal living human volunteers. After a preliminary discussion of such considerations as some fundamental differences between cross-sectional anatomy and conventional anatomy and the relationship between cross-sectional anatomy and computerized tomography, there is an explanation of the textual format. This section includes a description of the presentations on the pages, the use of color in the scans, and the use of certain advanced features of the ACTA-Scanner, the scanner used for the atlas. The final part of the article illustrates sample scans and a diagram from the forthcoming test.

Anatomy↗
PubMed1994

Cancer mapping: why not use absolute scales?

Cancer atlases frequently use relative scales which rank regional mortality rates relative to the countrywide mean (if relative risks are used) or median (if percentile colour schemes are used). This method has various disadvantages which limit comparability and interpretation. An alternative is absolute scaling which is well-known from geographic atlases: the natural rank order of altitudes is mapped into a colour scheme which is preserved over all maps of an atlas. Applied to cancer atlases this means that the different magnitudes of mortality for different cancer sites and sexes is preserved and leads to the use of different ranges of a relatively wide common colour scheme. This technique is applied to the data of the German Cancer Atlas published in 1984. Its properties are outlined and it is shown that it overcomes many of the unfavourable characteristics of the convenient procedures.

Atlases as Topic↗
PubMed2005

Toward atlas-assisted automatic interpretation of MRI morphological brain scans in the presence of tumor.

RATIONALE AND OBJECTIVES: Determination of distorted brain anatomy surrounding a tumor causing a mass effect is much more difficult than interpretation of normal brain scans, particularly because this distortion is not easily predictable a tumor may be located in any place and vary substantially in size, shape, and radiological appearance. The objective of our work is to provide a qualitative means for rapid estimation of brain anatomy distorted by tumor. MATERIALS AND METHODS: Toward achieving this objective, we use an electronic and deformable brain atlas of gross anatomy along with a fast atlas-to-data warping technique. The deformed atlas determines the distorted anatomy surrounding a tumor and can be used for structure labeling (naming). The warping algorithm uses the Talairach transformation followed by three-dimensional nonlinear tumor deformation based on a geometric assumption that the tumor, delineated on radiological images, compresses its surrounding tissues radially. RESULTS: The approach is implemented and a dedicated application is developed. It processes the data automatically in five steps: (1) load data, (2) set the Talairach landmarks and perform the Talairach transformation, (3) segment the tumor, (4) warp the scan nonlinearly in three dimensions, and (5) explore the scan. The approach is very fast, and a magnetic resonance imaging scan is processed in 10-15 seconds on a standard personal computer. It is fully automatic and gives the user control over the entire process. CONCLUSION: Despite its limitations in modeling and validation, this practical solution provides a rapid and potentially useful qualitative assessment of anatomy deformed by a mass effect tumor.

Anatomy, Artistic↗
PubMed2006

Analysis of ischemic stroke MR images by means of brain atlases of anatomy and blood supply territories.

RATIONALE AND OBJECTIVES: A method for atlas-assisted analysis of stroke magnetic resonance images that is a part of a stroke computer-assisted diagnosis system supporting rapid and quantitative checking of thrombolysis conditions is presented. MATERIALS AND METHODS: Two brain atlases are used for analysis: atlas of anatomy (AA) and atlas of blood supply territories (BSTs). To map these atlases onto scans, two methods are used at present: (1) fast Talairach transformation and (2) midsagittal plane and brain's bounding box matching. After atlas-to-scan mapping, both atlases are superimposed onto the studied images and can be used to get their underlying anatomy and BSTs. To speed up the process of analysis, the system automatically analyzes entire regions occupied by the infarct and penumbra. RESULTS: By using both atlases, the system calculates the following values for each infarct and penumbra region: (1) names of all anatomic structures and BSTs within the region, (2) volumes of occupancy for each structure and territory, and (3) percentages of occupancy for each structure and territory. In addition, the system calculates the infarct-middle cerebral artery (MCA) territory ratio for diffusion-weighted images and the penumbra-MCA territory ratio for perfusion images. Atlas-assisted analysis is fast, and calculations take less than 10 seconds. CONCLUSION: This method potentially facilitates and speeds up stroke data analysis, as well as supports decision making.

Atlases as Topic↗
PubMed2026

Essence: A benchmarking-validated transformer framework for early diagnosis of Parkinson's disease using cerebrospinal fluid protein biomarkers.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. The lack of objective molecular biomarkers limits early diagnosis and personalized treatment. Here, we propose Essence, a benchmarking-validated framework integrating cerebrospinal fluid (CSF) proteomics with traditional and deep learning models to identify robust protein signatures for PD. Using data from two independent cohorts, 1266 high-confidence proteins are quantified, among which 178 exhibit differential abundance between PD and healthy controls (HC). Through systematic benchmarking of ten machine learning algorithms and four neural architectures, the Transformer model consistently outperforms alternatives across multiple feature selection strategies, achieving an area under the receiver operating characteristic curve (AUC) of 1.0000 with only 35 features. Functional analyses of the top-ranked 35 proteins reveal enrichment in neuroinflammatory, synaptic, and oxidative stress-related pathways. Importantly, spatial transcriptomic profiling based on the Allen Brain Atlas shows region-specific expression of these biomarkers in PD-relevant brain structures, including the striatum, subthalamic nucleus, hippocampus, and white matter tracts. This anatomical alignment supports the functional relevance of the identified markers and highlights their potential utility in early-stage diagnosis and mechanistic understanding of PD.

Benchmarking↗
PubMed2003

Development and evaluation of an automated atlas-based image analysis method for microPET studies of the rat brain.

An automated method for placement of 3D rat brain atlas-derived volumes of interest (VOIs) onto PET studies has been designed and evaluated. VOIs representing major structures of the rat brain were defined on a set of digitized cryosectioned images of the rat brain. For VOI placement, each PET study was registered with a synthetic PET target constructed from the VOI template. Registration was accomplished with an automated algorithm that maximized the mutual information content of the image volumes. The accuracy and precision of this method for VOI placement was determined using datasets from PET studies of the striatal dopamine and hippocampal serotonin systems. Each evaluated PET study could be registered to at least one synthetic PET target without obvious failure. Registration was critically dependent upon the initial position of the PET study relative to the synthetic PET target, but not dependent on the amount of synthetic PET target smoothing. An evaluation algorithm showed that resultant radioactivity concentration measurements of selected brain structures had errors=2% due to misalignment with the corresponding VOI. Further, radioligand binding values calculated from these measurements were found to be more precise than those calculated from measurements obtained with manually drawn regions of interest (ROIs). Overall, evaluation results demonstrated that this atlas-derived VOI method can be used to obtain unbiased measurements of radioactivity concentration from PET studies. Its automated features, and applicability to different radioligands and brain regions, will facilitate quantitative rat brain PET assessment procedures.

Animals↗
PubMed2004

Surface-based approaches to spatial localization and registration in primate cerebral cortex.

Explicit surface reconstructions provide invaluable substrates for visualizing and analyzing the complex convolutions of cerebral cortex. This report illustrates the utility of surface-based atlases of human and macaque monkey for representing many aspects of cortical organization and function. These include a variety of cortical partitioning schemes plus an open-ended collection of complex activation patterns obtained from fMRI studies. Surface-based registration from one hemisphere to an atlas provides powerful approach to (i) objectively and quantitatively representing both the consistencies and the variability of the pattern of convolutions and the patterns of functional activation from any given task; and (ii) making comparisons across species and evaluating candidate homologies between cortical areas or functionally delineated regions.

Animals↗
PubMed2004

Unbiased diffeomorphic atlas construction for computational anatomy.

Construction of population atlases is a key issue in medical image analysis, and particularly in brain mapping. Large sets of images are mapped into a common coordinate system to study intra-population variability and inter-population differences, to provide voxel-wise mapping of functional sites, and help tissue and object segmentation via registration of anatomical labels. Common techniques often include the choice of a template image, which inherently introduces a bias. This paper describes a new method for unbiased construction of atlases in the large deformation diffeomorphic setting. A child neuroimaging autism study serves as a driving application. There is lack of normative data that explains average brain shape and variability at this early stage of development. We present work in progress toward constructing an unbiased MRI atlas of 2 years of children and the building of a probabilistic atlas of anatomical structures, here the caudate nucleus. Further, we demonstrate the segmentation of new subjects via atlas mapping. Validation of the methodology is performed by comparing the deformed probabilistic atlas with existing manual segmentations.

Algorithms↗
PubMed2005

A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex.

This report describes a new electronic atlas of human cerebral cortex that provides a substrate for a wide variety of brain-mapping analyses. The Population-Average, Landmark- and Surface-based (PALS) atlas approach involves surface-based and volume-based representations of cortical shape, each available as population averages and as individual subject data. The specific PALS-B12 atlas introduced here is derived from structural MRI volumes of 12 normal young adults. Accurate cortical surface reconstructions were generated for each hemisphere, and the surfaces were inflated, flattened, and mapped to standard spherical configurations using SureFit and Caret software. A target atlas sphere was generated by averaging selected landmark contours from each of the 24 contributing hemispheres. Each individual hemisphere was deformed to this target using landmark-constrained surface registration. The utility of the resultant PALS-B12 atlas was demonstrated using a variety of analyses. (i) Probabilistic maps of sulcal identity were generated using both surface-based registration (SBR) and conventional volume-based registration (VBR). The SBR approach achieved markedly better consistency of sulcal alignment than did VBR. (ii) A method is introduced for 'multi-fiducial mapping' of volume-averaged group data (e.g., fMRI data, probabilistic architectonic maps) onto each individual hemisphere in the atlas, followed by spatial averaging across the individual maps. This yielded a population-average surface representation that circumvents the biases inherent in choosing any single hemisphere as a target. (iii) Surface-based and volume-based morphometry applied to maps of sulcal depth and sulcal identity demonstrated prominent left-right asymmetries in and near the superior temporal sulcus and Sylvian fissure. Moreover, shape variability in the temporal lobe is significantly greater in the left than the right hemisphere. The PALS-B12 atlas has been registered to other surface-based atlases to facilitate interchange of data and comparison across atlases. All data sets in the PALS-B12 atlas are accessible via the SumsDB database for online and offline visualization and analysis.

Adolescent↗
PubMed2006

Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms.

Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and intra-individual deformations. The paper first describes a statistical approach to capturing inter-individual variability of high-deformation fields from a number of examples (training samples). In this approach, Wavelet-Packet Transform (WPT) of the training deformations and their Jacobians, in conjunction with a Markov random field (MRF) spatial regularization, are used to capture both coarse and fine characteristics of the training deformations in a statistical fashion. Simulated deformations can then be constructed by randomly sampling the resultant statistical distribution in an unconstrained or a landmark-constrained fashion. The paper also describes a model for generating tissue atrophy or growth in order to simulate intra-individual brain deformations. Several sets of simulated deformation fields and respective images are generated, which can be used in the future for systematic and extensive validation studies of automated atlas-based segmentation and deformable registration methods. The code and simulated data are available through our Web site.

Algorithms↗
PubMed2002

A global livestock production and health atlas (GLiPHA) for interactive presentation, integration and analysis of livestock data.

An interactive electronic atlas has been developed with the purpose of providing a scaleable overview of spatial and temporal variation in animal production and health-related information for decision and policy makers in national and international institutions. The information contained in the atlas is currently managed and presented using the Key Indicators Mapping System (KIMS), and will also be integrated using the Key Indicators Database System (KIDS). Both systems were developed by the World Agricultural Information Centre of the FAO (FAO-WAICENT), the former as a stand-alone application and the latter for access via the Internet. Components of the atlas include vector maps, livestock disease and production databases, rules for country-level disease risk classification and 'disease cards' containing basic background information on diseases included in the atlas. The disease data is currently based primarily on Office International des Epizooties (OIE) disease reports, and the livestock production data on the FAO-WAICENT database. The atlas is highly interactive and allows visual presentation of information using maps, tables and charts. It also contains links to relevant resource information on the Internet. Diseases covered in the animal health layer include most OIE List A diseases and a subset of OIE List B diseases. Extensive analyses have been conducted to develop a set of qualitative and semi-quantitative criteria that allow improved disease status classifications based on 5-years cumulative OIE disease reports, and official disease control declarations. Classification rules were determined depending on the epidemiological features of each disease and considering spatial heterogeneity of disease presence in local regions.

Animal Diseases↗