Sulcal basins and sulcal strings as new concepts for describing the human cortical topography (original) (raw)
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Automatic labelling of the human cortical surface using sulcal basins
Medical Image Analysis, 2000
Human brain mapping aims at establishing correspondences between brain function and brain anatomy. One of the most intriguing problems in this field is the high interpersonal variability of human neuroanatomy which makes studies across many subjects very difficult. The cortical folds ('sulci') often serve as landmarks that help to establish correspondences between subjects. In this paper, we will present a method that automatically detects and attributes neuroanatomical names to the cortical folds using image analysis methods applied to magnetic resonance data of human brains. We claim that the cortical folds can be subdivided into a number of substructures which we call sulcal basins. The concept of sulcal basins allows us to establish a complete parcellation of the cortical surface into separate regions. These regions are neuroanatomically meaningful and can be identified from MR data sets across many subjects. Sulcal basins are segmented using a region growing approach. The automatic labelling is achieved by a model matching technique.
Automatic detection and labelling of the human cortical folds in magnetic resonance data sets
Computer VisionECCV'98, 1998
The folding of the cortical surface of the human brain varies dramatically from person to person. However, the folding pattern is not arbitrary. The cortical folds (also called "sulci') often serve as landmarks for referencing brain locations, and the most pronounced sulci have names that are well established in the neuroanatomical literature. In this paper, we will present a method that both automatically detects and attributes neuroanatomical names to these folds using image analysis methods applied to magnetic resonance data of human brains. More precisely, we subdivide each fold into a number of substructures which we call sulcal basins, and attach labels to these basins. These sulcal basins form a complete parcellation of the cortical surface. The algorithm reported here is important in the context of human brain mapping. Human brain mapping aims at establishing correspondences between brain function and brain anatomy. One of the most intriguing problems in this field is the high inter-personal variability of human neuroanatomy which makes studies across many subjects very difficult. Most previous attempts at solving this problem are based on various methods of image registration where MR data sets of different subjects are warped until they overlap. We believe that in the process of warping too much of the individual anatomy is destroyed so that relevant information is lost. The approach presented in this paper allows inter-personal comparisons without having to resort to image warping. Our concept of sulcal basins allows to establish a complete parcellation of the cortical surface into separate regions. These regions axe neuroanatomically meaningful and can be identified from MR data sets across many subjects. At the same time, the parcellation is detailed enough to be useful for brain mapping purposes.
Automatic classification of sulcal regions of the human brain cortex using pattern recognition
Medical Imaging 2003: Image Processing, 2003
Parcellation of the cortex has received a great deal of attention in magnetic resonance (MR) image analysis, but its usefulness has been limited by time-consuming algorithms that require manual labeling. An automatic labeling scheme is necessary to accurately and consistently parcellate a large number of brains. The large variation of cortical folding patterns makes automatic labeling a challenging problem, which cannot be solved by deformable atlas registration alone. In this work, an automated classification scheme that consists of a mix of both atlas driven and data driven methods is proposed to label the sulcal regions, which are defined as the gray matter regions of the cortical surface surrounding each sulcus. The premise for this algorithm is that sulcal regions can be classified according to the pattern of anatomical features (e.g. supramarginal gyrus, cuneus, etc.) associated with each region. Using a nearest-neighbor approach, a sulcal region is classified as being in the same class as the sulcus from a set of training data which has the nearest pattern of anatomical features. Using just one subject as training data, the algorithm correctly labeled 83% of the regions that make up the main sulci of the cortex.
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature
NeuroImage, 2010
Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computerassisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology.
Watersheds on the cortical surface for automated sulcal segmentation
NeuroImage, 2002
The human cortical surface is a highly complex, folded structure. Sulci, the spaces between the folds, define location on the cortex and provide a parcellation into anatomically distinct areas. A topic that has recently received increased attention is the segmentation of these sulci from magnetic resonance images, with most work focusing on extracting either the sulcal spaces between the folds or curve representations of sulci. Unlike these methods, we propose a technique that extracts actual regions of the cortical surface that surround sulci, which we call "sulcal regions." The method is based on a watershed algorithm applied to a geodesic depth measure on the cortical surface. A well-known problem with the watershed algorithm is a tendency toward oversegmentation, meaning that a single region is segmented as several pieces. To address this problem, we propose a postprocessing algorithm that merges appropriate segments from the watershed algorithm. The sulcal regions are then manually labeled by simply selecting the appropriate regions with a mouse click and a preliminary study of sulcal depth is reported. Finally, a scheme is presented for computing a complete parcellation of the cortical surface. , available online at http://www.idealibrary.com on FIG. 5. Cross-sectional view of (a) the edge map generated from the cortical surface, (b) the deformable surface breaking through gyral regions, (c) the barrier region, and (d) the shrink-wrap surface superimposed on the true cortical surface.
Cerebral cortex: a topographic segmentation method using magnetic resonance imaging
Psychiatry Research: Neuroimaging, 2000
. Remarkable developments in magnetic resonance imaging MRI technology provide a broad range of potential applications to explore in vivo morphological characteristics of the human cerebral cortex. MR-based parcellation methods of the cerebral cortex may clarify the structural anomalies in specific brain subregions that reflect underlying neuropathological processes in brain illnesses. The present study describes detailed guidelines for the parcellation of the cerebral cortex into 41 subregions. Our method conserves the topographic uniqueness of individual brains and is based on our ability to visualize the three orthogonal planes, the triangulated gray matter Ž . isosurface and the three-dimensional 3D rendered brain simultaneously. Based upon topographic landmarks of individual sulci, every subregion was manually segmented on a set of serial coronal or transaxial slices consecutively. The reliability study indicated that the cerebral cortex could be parcelled reliably; intraclass correlation coefficients for each subregion ranged from 0.60 to 0.99. The validity of the method is supported by the fact that gyral subdivisions are similar to regions delineated in functional imaging studies conducted in our center. Ultimately, this method will permit us to detect subtle morphometric impairments or to find abnormal patterns of functional activation in circumscribed cortical subregions. The description of a thorough map of regional structural and functional cortical abnormalities will provide further insight into the role that different subregions play in the pathophysiology of brain illnesses. ᮊ
Journal of Anatomy, 1998
Guided by a review of the anatomical literature, 36 sulci on the human cerebral cortical surface were designated as homologous. These sulci were assessed for visibility on 3-dimensional images reconstructed from magnetic resonance imaging scans of the brains of 20 normal volunteers by 2 independent observers. Those sulci that were found to be reproducibly identifiable were used to define 24 landmarks around the cortical surface. The interobserver and intraobserver variabilities of measurement of the 24 landmarks were calculated. These reliably reproducible landmarks can be used for detailed morphometric analysis, and may prove helpful in the analysis of suspected cerebral cortical structured abnormalities in patients with such conditions as epilepsy.
Cortical surface segmentation and mapping
NeuroImage, 2004
Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical thickness changes in aging are presented. D 2004 Elsevier Inc. All rights reserved.
Automated Sulcal Segmentation Using Watersheds on the Cortical Surface
Neuroimage, 2002
The human cortical surface is a highly complex, folded structure. Sulci, the spaces between the folds, define location on the cortex and provide a parcellation into anatomically distinct areas. A topic that has recently received increased attention is the segmentation of these sulci from magnetic resonance images, with most work focusing on extracting either the sulcal spaces between the folds or curve representations of sulci. Unlike these methods, we propose a technique that extracts actual regions of the cortical surface that surround sulci, which we call "sulcal regions." The method is based on a watershed algorithm applied to a geodesic depth measure on the cortical surface. A well-known problem with the watershed algorithm is a tendency toward oversegmentation, meaning that a single region is segmented as several pieces. To address this problem, we propose a postprocessing algorithm that merges appropriate segments from the watershed algorithm. The sulcal regions are then manually labeled by simply selecting the appropriate regions with a mouse click and a preliminary study of sulcal depth is reported. Finally, a scheme is presented for computing a complete parcellation of the cortical surface. , available online at http://www.idealibrary.com on FIG. 5. Cross-sectional view of (a) the edge map generated from the cortical surface, (b) the deformable surface breaking through gyral regions, (c) the barrier region, and (d) the shrink-wrap surface superimposed on the true cortical surface.