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Conference on Medical Imaging - Physics of Medical Imaging

作者： Fischer, Bjoern Krueger, Peter Poznyakovskiy, Anton A. Zahnert, Thomas Dresden Branch IZFP Fraunhofer Inst Nondestruct Testing Maria Reiche Str 2 D-01109 Dresden Germany Univ Dresden Clin Oto Rhino Laryngol D-01307 Dresden Germany

ISBN: (纸本)9780819489623

Validation of statistically confirmed geometrical models of the human inner ear requires precise image data of bony structures and soft tissue. By optimization and application of our Industrial High Resolution Micro Computer Tomograph (microCT, Fraunhofer IZFP), we achieve an image quality known so far only from synchrotron X-ray tomography. To improve the generation of X-ray photons, the micro focus X-ray tube was equipped with a high performance transmission target with diamond carrier to achieve a higher photon flux at the same focal diameter. Additionally, our new alignment tool allows post-acquisition correction of adjustment incorrectness which plays a key role in reconstruction of high resolution microCT-data. However, an accurate segmentation of the cochlea is still problematic due to the low X-ray absorption contrast of the inner membranes of scalae. To solve this problem, the soft tissue was stained by putting the specimen into an iodine solution. The resulting data are of outstanding quality and provide the basin for an extensive anatomical study and further development of statistical geometrical models of the human inner ear.

关键词： CI microCT Diamond X-Ray Transmission Target post-acquisition collection correction X-ray absorption contrast staining of soft tissue atlas-based segmentation

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OPTIMAL atlas SELECTION USING IMAGE SIMILARITIES IN A TRAINED REGRESSION MODEL TO PREDICT PERFORMANCE

OPTIMAL ATLAS SELECTION USING IMAGE SIMILARITIES IN A TRAINE...

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9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Akinyemi, Akin Plakas, Costas Piper, Jim Roberts, Colin Poole, Ian Toshiba Medical Visualization Systems Europe

ISBN: (纸本)9781457718588

An atlas in the context of atlas-based segmentation refers to a pre-selected image with labelled anatomical regions of interest. atlas-based segmentation is the propagation of these labels to a novel image after both images have been registered. The goal of an atlas is to be representative of an anatomical category, but in practice there exists variability in human anatomy. One solution to maintain consistent segmentation accuracies is to use multiple atlases, with a system for selecting the most appropriate atlas at the time of segmentation. This paper describes a method for selecting an atlas using a linear regression model to predict the segmentation accuracy based on image similarity measures. It goes further to present an offline method for automatically selecting a set of atlases, representative of the training set to be used during segmentation;all of this illustrated by segmentation of the heart and kidneys in 3D CT images.

关键词： atlas-based segmentation multi-atlas optimal atlas selection registration

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Deformation based morphmetry and atlas based label segmentation: Application to serial mouse brain images

Deformation based morphmetry and atlas based label segmentat...

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5th IEEE International Symposium on Biomedical Imaging

作者： Maheswaran, Satheesh Barjat, Herve Bate, Simon T. Hartkens, Thomas Hill, Derek L. G. Tilling, Loma Upton, Neil James, Michael F. Hajnal, Joseph V. Rueckert, Daniel London Imperial Coll Dept Comp 180 Queens GateS Kensington Campus London SW7 2AZ England Neurol & Gastrointestinal Ctr Excellence Drug Discovery Harlow Essex CM19 5AW England GlaxoSmithKline Stat Sci Harlow Essex CM19 5AW England IXICO Ltd London Biosci Innovat Ctr London NW1 0NH England Hammersmith Hosp Imperial Coll London Imaging Sci Dept London W12 0NN England

ISBN: (纸本)9781424420025

The aim of this paper is to investigate techniques that can identify and quantify longitudinal changes in vivo from magnetic resonance (MR) images of murine models of brain disease. Two different approaches have been compared. The first approach is a segmentation-based approach: Each subject at each time point is automatically segmented into a number of anatomical structures using atlas-based segmentation. This allows longitudinal analyses of group differences on a structure-by-structure basis. The second approach is a deformation-based approach: Longitudinal changes are quantified via registration of each subject's follow-up images to that subject's baseline image. Both approaches have been tested on two groups of mice: A transgenic model of Alzheimer's disease and a wild-type background strain, using serial imaging performed over the age range from 6-14 months. We show that both approaches are able to identify longitudinal differences. However, atlas-based segmentation suffers from the inability to detect differences across populations and across time in regions which are much smaller than the anatomical regions. In contrast to this, the deformation-based approach can detect statistically significant differences in highly localized areas.

关键词： atlas-based segmentation deformation based morphometry (DBM) longitudinal image analysis serial mouse imaging

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Optimum template selection for atlas-based segmentation

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NEUROIMAGE 2007年第4期34卷 1612-1618页

作者： Wu, Minjie Rosano, Caterina Lopez-Garcia, Pilar Carter, Cameron S. Aizenstein, Howard J. Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15213 USA Univ Pittsburgh Western Psychiat Inst & Clin Dept Psychiat Pittsburgh PA 15213 USA Univ Pittsburgh Dept Epidemiol Pittsburgh PA 15213 USA Univ Calif Davis Dept Psychiat Sacramento CA 95817 USA

atlas-based segmentation of MR brain images typically uses a single atlas (e.g., MNI Colin27) for region identification. Normal individual variations in human brain structures present a significant challenge for atlas selection. Previous researches mainly focused on how to create a specific template for different requirements (e.g., for a certain population). We address atlas selection with a different approach: instead of choosing a fixed brain atlas, we use a family of brain templates for atlas-based segmentation. For each subject and each region, the template selection method automatically chooses the 'best' template with the highest local registration accuracy, based on normalized mutual information. The region classification performances of the template selection method and the single template method were quantified by the overlap ratios (ORs) and intraclass correlation coefficients (ICCs) between the manual tracings and the respective automated labeled results. Two groups of brain images and multiple regions of interest (ROIs), including the right anterior cingulate cortex (ACC) and several subcortical structures, were tested for both methods. We found that the template selection metho produced significantly higher ORs than did the single template method across all of the 13 analyzed ROIs (two-tailed paired t-test, right ACC at t(8)=4.353, p=0.0024;right amygdala, matched paired t test t(8)> 3.175, p < 0.013;for the remaining ROIs, t(8) = 4.36, p < 0.002). The template selection method also provided more reliable volume estimates than the single template method with increased ICCs. Moreover, the improved accuracy of atlas-based segmentation using optimum templates approaches the accuracy of manual tracing, and thus is valid for automated brain imaging analyses. (c) 2006 Elsevier Inc. All rights reserved.

关键词： atlas-based segmentation template selection

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Automatic segmentation of neonatal images using convex optimization and coupled level sets

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NEUROIMAGE 2011年第3期58卷 805-817页

作者： Wang, Li Shi, Feng Lin, Weili Gilmore, John H. Shen, Dinggang Univ N Carolina Dept Radiol IDEA Lab Chapel Hill NC 27599 USA Univ N Carolina BRIC Chapel Hill NC 27599 USA Univ N Carolina Dept Radiol MRI Lab Chapel Hill NC 27599 USA Univ N Carolina Dept Psychiat Chapel Hill NC 27599 USA

Accurate segmentation of neonatal brain MR images remains challenging mainly due to their poor spatial resolution, inverted contrast between white matter and gray matter, and high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although active contour/surface models with geometric information constraint have been successfully applied to adult brain segmentation, they are not fully explored in the neonatal image segmentation. In this paper, we propose a novel neonatal image segmentation method by combining local intensity information, atlas spatial prior, and cortical thickness constraint in a single level-set framework. Besides, we also provide a robust and reliable tissue surface initialization for the proposed method by using a convex optimization technique. Thus, tissue segmentation, as well as inner and outer cortical surface reconstruction, can be obtained simultaneously. The proposed method has been tested on a large neonatal dataset, and the validation on 10 neonatal brain images (with manual segmentations) shows very promising results. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Neonatal tissue segmentation atlas-based segmentation Convex optimization Coupled level sets

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Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy

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EPILEPSY RESEARCH 2011年第1-2期95卷 35-50页

作者： Jafari-Khouzani, Kourosh Elisevich, Kost Karvelis, Kastytis C. Soltanian-Zadeh, Hamid Henry Ford Hosp Dept Diagnost Radiol Detroit MI 48202 USA Henry Ford Hosp Dept Neurosurg Detroit MI 48202 USA Univ Tehran Control & Intelligent Proc Ctr Excellence Elect & Comp Engn Dept Tehran 14395515 Iran

This study assesses the utility of compartmental analysis of SPECT data in lateralizing ictal onset in cases of a putative mesial temporal lobe epilepsy (mTLE). An institutional archival review provided 46 patients (18M, 28F) operated for a putative mTLE who achieved an Engel class la postoperative outcome. This established the standard to assure a true ictal origin. Ictal and interictal SPECT images were separately coregistered to T1-weighted (T1W) magnetic resonance (MR) image using a rigid transformation and the intensities matched with an l(1) norm minimization technique. The T1W MR image was segmented into separate structures using an atlas-based automatic segmentation technique with the hippocampi manually segmented to improve accuracy. Mean ictal-interictal intensity difference values were calculated for select subcortical structures and the accuracy of lateralization evaluated using a linear classifier. Hippocampal SPECT analysis yielded the highest lateratization accuracy (91%) followed by the amygdala (87%), putamen (67%) and thalamus (61%). Comparative FLAIR and volumetric analyses yielded 89% and 78% accuracies, respectively. A multi-modality analysis did not generate a higher accuracy (89%). A quantitative anatomically compartmented approach to SPECT analysis yields a particularly high lateralization accuracy in the case of mTLE comparable to that of quantitative FLAIR MR imaging. Hippocampal segmentation in this regard correlates well with ictal origin and shows good reliability in the preoperative analysis. (c) 2011 Elsevier B.V. All rights reserved.

关键词： Ictal onset Quantification atlas-based segmentation Hippocampus SPECT Normalization

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Supervised segmentation methods for the hippocampus in MR images

Supervised segmentation methods for the hippocampus in MR im...

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Conference on Medical Imaging 2011 - Image Processing

作者： van Stralen, Marijn Geerlings, Mirjam I. Vincken, Koen L. Pluim, Josien P. W. UMC Utrecht Image Sci Inst Room QS 459Heidelberglaan 100 NL-3584 CX Utrecht Netherlands UMC Utrecht NL-3584 CX Utrecht Netherlands

ISBN: (纸本)9780819485045

This study compares three different types of fully automated supervised methods for segmentation of the hippocampus in MR images. Many of such methods, trained using example data, have been presented for various medical imaging applications, but comparison of the methods is obscured because of optimization for, and evaluation on, different data. We compare three methods based on different methodological bases: atlas-based segmentation (ABS), active appearance model segmentation (AAM) and k-nearest neighbor voxel classification (KNN). All three methods are trained on 100 T1-weighted images with manual segmentations of the right hippocampus, and applied to 103 different images from the same study. Straightforward implementation of each of the three methods resulted in competitive segmentations, both mutually, as compared with methods currently reported in literature. AAM and KNN are favorable in terms of computational costs, requiring only a fraction of the time needed for ABS. The high accuracy and low computational cost make KNN the most favorable method based on this study. AAM achieves similar results as ABS in significantly less computation time. Further improvements might be achieved by fusion of the presented techniques, either methodologically or by direct fusion of the segmentation results.

关键词： segmentation active appearance model atlas-based segmentation k-nearest neighbor voxel classification hippocampus

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AUTOMATIC SUBCORTICAL TISSUE segmentation OF MR IMAGES USING OPTIMUM-PATH FOREST CLUSTERING

AUTOMATIC SUBCORTICAL TISSUE SEGMENTATION OF MR IMAGES USING...

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18th IEEE International Conference on Image Processing (ICIP)

作者： Cappabianco, Fabio Ide, Jaime S. Falcao, Alexandre Li, Chiang-shan R. Univ Fed Sao Paulo Dept Ciencia & Tecnol BR-12231 Sao Jose Dos Campos SP Brazil Univ Estadual Campinas Inst Comp BR-13083 Campinas SP Brazil Yale Univ Sch Med Dept Psychiat New Haven CT 06519 USA

ISBN: (纸本)9781457713033

Automatic MR-image segmentation of brain tissues is an important issue in neuroimaging. For instance, it is a key methodological component of a popular technique denominated voxel-based morphometry (VBM), which quantifies gray-matter (GM) volumes from MR images. However, segmentation accuracy in some subcortical regions on the basis of extant methods is not satisfactory, compromising VBM results. We combine a probabilistic atlas and a fast clustering approach based on optimum connectivity between voxels in their feature space. The algorithm exploits local image properties and global information from the atlas as features to group GM and white-matter (WM) voxels in distinct clusters, and uses the total probability values inside the clusters to label them as GM or WM. This new method is validated in the region of the thalamus and outperformed two widely used methods packaged in SPM and FSL.

关键词： medical image analysis atlas-based segmentation clustering graph-search algorithms for image processing mri

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segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-based atlas Registration

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2009年第1期3卷 135-147页

作者： Gorthi, Subrahmanyam Duay, Valerie Houhou, Nawal Cuadra, Meritxell Bach Schick, Ulrike Becker, Minerva Allal, Abdelkarim S. Thiran, Jean-Philippe Ecole Polytech Fed Lausanne EPFL Signal Proc Lab LTS5 Lausanne Switzerland Univ Appl Sci Western Switzerland HES SO Geneve Inst Bioengn Geneva Switzerland Univ Hosp Geneva Dept Radiat Oncol Geneva Switzerland

In this paper, we present the segmentation of the head and neck lymph node regions using a new active contour-based atlas registration model. We propose to segment the lymph node regions without directly including them in the atlas registration process;instead, they are segmented using the dense deformation field computed from the registration of the atlas structures with distinct boundaries. This approach results in robust and accurate segmentation of the lymph node regions even in the presence of significant anatomical variations between the atlas-image and the patient's image to be segmented. We also present a quantitative evaluation of lymph node regions segmentation using various statistical as well as geometrical metrics: sensitivity, specificity, dice similarity coefficient and Hausdorff distance. A comparison of the proposed method with two other state of the art methods is presented. The robustness of the proposed method to the atlas selection, in segmenting the lymph node regions, is also evaluated.

关键词： atlas-based segmentation head and neck IMRT lymph node regions non-rigid registration radiotherapy

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Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2006年第2-3期84卷 66-75页

作者： Cuadra, M. Bach De Craene, M. Duay, V. Macq, B. Pollo, C. Thiran, J. -Ph. Ecole Polytech Fed Lausanne Signal Proc Inst ITS CH-1015 Lausanne Switzerland Catholic Univ Louvain Commun Lab B-1348 Louvain Belgium Univ Lausanne Hosp Dept Neurosurg CH-1011 Lausanne Switzerland

atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art. (c) 2006 Elsevi

关键词： atlas-based segmentation brain tumors MR imaging registration

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