Translate this page to:
Turkish
In JoVE (1)
- Yerel Gyrification Endeksi hesapla Adım Adım Öğretici: MR Görüntüleri Kortikal Katlama nasıl ölçülür
Other Publications (14)
- IEEE Transactions on Medical Imaging
- NeuroImage
- IEEE Transactions on Medical Imaging
- Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
- IEEE Transactions on Medical Imaging
- IEEE Transactions on Medical Imaging
- Medical Image Computing and Computer-assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
- Developmental Medicine and Child Neurology
- Computer Methods and Programs in Biomedicine
- Schizophrenia Research
- Journal of Neurodevelopmental Disorders
- Medical Image Analysis
- Medical Image Analysis
- Computer Methods and Programs in Biomedicine
Automatic Translation
This translation into Turkish was automatically generated.
English Version | Other Languages
Articles by Meritxell Bach Cuadra in JoVE
JoVE Clinical and Translational Medicine
Yerel Gyrification Endeksi hesapla Adım Adım Öğretici: MR Görüntüleri Kortikal Katlama nasıl ölçülür
1Department of Psychiatry, University of Geneva School of Medicine, 2Signal Processing Laboratory, École Polytechnique Fédérale de Lausanne, 3Department of Radiology, University Hospital Center and University of Lausanne, 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Ölçüm gyrification (kortikal katlama) herhangi bir yaşta, erken beyin gelişiminde bir pencere temsil eder. Bu nedenle, daha önce yarımkürede üzerinde puan binlerce yerel gyrification ölçmek için bir algoritma geliştirdi
Other articles by Meritxell Bach Cuadra on PubMed
Atlas-based Segmentation of Pathological MR Brain Images Using a Model of Lesion Growth
We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.
Segmentation of Brain Structures in Presence of a Space-occupying Lesion
Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.
Comparison and Validation of Tissue Modelization and Statistical Classification Methods in T1-weighted MR Brain Images
This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.
Cross Validation of Experts Versus Registration Methods for Target Localization in Deep Brain Stimulation
In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatent of Parkinson's disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.
A Cross Validation Study of Deep Brain Stimulation Targeting: from Experts to Atlas-based, Segmentation-based and Automatic Registration Algorithms
Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper, we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intraoperatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on magnetic resonance (MR) T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and nonrigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross validated between them and against expert's variability. Using this scheme, we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.
A Surface-based Approach to Quantify Local Cortical Gyrification
The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11 Deletion Syndrome, compared to control individuals.
An Active Contour-based Atlas Registration Model Applied to Automatic Subthalamic Nucleus Targeting on MRI: Method and Validation
This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.
Congenital Heart Disease Affects Local Gyrification in 22q11.2 Deletion Syndrome
22q11.2 deletion syndrome (22q11.2DS) is a common genetic condition associated with cognitive and learning impairments. In this study, we applied a three-dimensional method for quantifying gyrification at thousands of points over the cortical surface to imaging data from 44 children, adolescents, and young adults with 22q11.2DS (17 males, 27 females; mean age 17y 2mo [SD 9y 1mo], range 6-37y), and 53 healthy participants (21 males, 32 females; mean age 15y 4mo [SD 8y 6mo]; range 6-40y). Several clusters of reduced gyrification were observed, further substantiating the pattern of cerebral alterations presented by children with the syndrome. Comparisons within 22q11.2DS demonstrated an effect of congenital heart disease (CHD) on cortical gyrification, with reduced gyrification at the parieto-temporo-occipital junction in patients with CHD, as compared with patients without CHD. Reductions in gyrification can resemble mild polymicrogyria, suggesting early abnormal neuronal proliferation or migration and providing support for an effect of hemodynamic factors on brain development in 22q11.2DS. The results also shed light on the pathophysiology of acquired brain injury in other populations with CHD.
A Multidimensional Segmentation Evaluation for Medical Image Data
Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.
Deviant Trajectories of Cortical Maturation in 22q11.2 Deletion Syndrome (22q11DS): a Cross-sectional and Longitudinal Study
22q11.2 deletion syndrome (22q11DS) is associated with an increased susceptibility to develop schizophrenia. Despite a large body of literature documenting abnormal brain structure in 22q11DS, cerebral changes associated with brain maturation in 22q11DS remained largely unexplored. To map cortical maturation from childhood to adulthood in 22q11.2 deletion syndrome, we used cerebral MRI from 59 patients with 22q11DS, aged 6 to 40, and 80 typically developing controls; three year follow-up assessments were also available for 32 patients and 31 matched controls. Cross-sectional cortical thickness trajectories during childhood and adolescence were approximated in age bins. Repeated-measures were also conducted with the longitudinal data. Within the group of patients with 22q11DS, exploratory measures of cortical thickness differences related to COMT polymorphism, IQ, and schizophrenia were also conducted. We observed deviant trajectories of cortical thickness changes with age in patients with 22q11DS. In affected preadolescents, larger prefrontal thickness was observed compared to age-matched controls. Afterward, we observed greater cortical loss in 22q11DS with a convergence of cortical thickness values by the end of adolescence. No compelling evidence for an effect of COMT polymorphism on cortical maturation was observed. Within 22q11DS, significant differences in cortical thickness were related to cognitive level in children and adolescents, and to schizophrenia in adults. Deviant trajectories of cortical thickness from childhood to adulthood provide strong in vivo cues for a defect in the programmed synaptic elimination, which in turn may explain the susceptibility of patients with 22q11DS to develop psychosis.
Regional Cortical Volumes and Congenital Heart Disease: a MRI Study in 22q11.2 Deletion Syndrome
Children with congenital heart disease (CHD) who survive surgery often present impaired neurodevelopment and qualitative brain anomalies. However, the impact of CHD on total or regional brain volumes only received little attention. We address this question in a sample of patients with 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition frequently associated with CHD. Sixty-one children, adolescents, and young adults with confirmed 22q11.2 deletion were included, as well as 80 healthy participants matched for age and gender. Subsequent subdivision of the patients group according to CHD yielded a subgroup of 27 patients with normal cardiac status and a subgroup of 26 patients who underwent cardiac surgery during their first years of life (eight patients with unclear status were excluded). Regional cortical volumes were extracted using an automated method and the association between regional cortical volumes, and CHD was examined within a three-condition fixed factor. Robust protection against type I error used Bonferroni correction. Smaller total cerebral volumes were observed in patients with CHD compared to both patients without CHD and controls. The pattern of bilateral regional reductions associated with CHD encompassed the superior parietal region, the precuneus, the fusiform gyrus, and the anterior cingulate cortex. Within patients, a significant reduction in the left parahippocampal, the right middle temporal, and the left superior frontal gyri was associated with CHD. The present results of global and regional volumetric reductions suggest a role for disturbed hemodynamic in the pathophysiology of brain alterations in patients with neurodevelopmental disease and cardiac malformations.
On the Convergence of EM-like Algorithms for Image Segmentation Using Markov Random Fields
Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.
Active Deformation Fields: Dense Deformation Field Estimation for Atlas-based Segmentation Using the Active Contour Framework
This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.
A Review of Atlas-based Segmentation for Magnetic Resonance Brain Images
Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.
