Atlas-based Segmentation Approach Research Articles

Research status of cardiac image segmentation based on deep learning

面对严重的医学影像分析缺口，深度学习的发展能够满足国内医疗行业的需求。心脏图像的处理方法可大致分为传统的图像处理技术、基于图谱的方法（atlas-based methods）、基于模型的方法（model-based methods）以及目前热门的采用机器学习和深度学习的方法。在深度学习兴起之前，传统的机器学习技术如模型法和图集法在心脏图像分割中有良好表现，但通常需要大量的特征工程知识或先验知识才能获得令人满意的精度。而基于深度学习的算法能从数据中自动发现复杂的特征以进行对象检测和分割。得益于先进的计算机硬件以及更多可用于训练的数据集，基于深度学习的分割算法已超越了以往的传统方法。本文回顾了2012—2022年有关心室、心外膜和心包脂肪的图像处理的各项方法、衡量指标及其目前的研究现状，并结合分割技术的发展，讨论了心脏分割的发展趋势。;Human-cardiovascular disease is challenged for its high morbidity and severe sequelae nowadays. To meet the need of the medical industry, current medical image analysis is facilitated via the development of deep learning. Conventional image processing technology is processed basically in terms of thresholding. The emerging deep learning technique can be focused on reality-oriented function in terms of specific eigenvalues. Such deep residual network and generative confrontation network have its potentials for its effectiveness and robust originated from good learning ability and data-driven factors. Our critical analysis is based on 1) characteristics of representative methods, 2) resources and scale of cardiac images, 3) comparative study of the performance evaluation and application conclusions of different methods through popular evaluation indicators, and 4) clinical domains are discussed as well. The literature review is originated from IEEE, SPIE, and China National Knowledge Network, with image processing and heart as search keywords. The difference between image processing methods is evaluated in terms of Dice coefficient and Hausdorff distance, and the performance is evaluated in quantitative further. For chamber segmentation, several approaches for right ventricle segmentation are reviewed and analyzed. As far as the principle of the segmentation method is concerned, the single of threshold is still challenged for segmentation unless it is integrated into other related methods, so it cannot be used as a single zone in segmentation technique. A brief theoretical introduction is mentioned for each method. Then, its methodology, prior datasets, and the effectiveness of segmentation process are involved in evaluation. Finally, the pros and cons of each method are analyzed as well. For the domain of epicardium and pericardium tissue, we will briefly introduce the popular image processing techniques for segmenting epicardium and pericardium tissue. Four category of key methods are analyzed in relevance to its:traditional image processing methods, atlas-based methods, machine learning, and deep learning. Traditional image processing methods are composed of such techniques of thresholding, region growing, and active contouring. Finally, Dice coefficient-derived capabilities of each algorithm are compared horizontally. For the segmentation method of the epicardium, it is easier to segment the epicardium into pericardium-illustrated coordination. Epicardial and pericardial fatty tissue are unevenly distributed around the heart, resulting in large sections-between variability and the images-between for its computed tomography (CT) and magnetic resonance imaging (MRI). The heterogeneity in shape is required to demonstrate further. However, the pericardium is featured of more smoother, thinner and oval in CT and MRI images. Such methods of active contours or ellipse fitting are suitable for segmenting such shapes naturally. Once the pericardium is divided, the epicardium is more easily divided into all pericardium-within fatty tissue. The great challenge is focused on epicardiumthinner segmentation. The slice of thickness can be set at 2~3 mm when CT scans are collected for coronary artery calcification (CAC) scoring. The pericardium is usually less than 2 mm thick, and it will often appear blurred or blurred on CT images in accordance with partial volume averaging, especially for heart organ-moving consistency. Some methods are purely pericardial delineation methods, while others are part of a method to segment and quantify the epicardium. For the epicardium part, we will mainly introduce the method of epicardium segmentation by the first pericardium-segmented. Pericardial fat segmentation methods typically rely on traditional image processing methods, such as 1) thresholding and region growing, and 2) various preprogrammed heuristics can be used to identify common structures and segment pericardial fat. Recent atlas-based segmentation approaches are employed but its clinical ability is relatively weakened. After current situation of segmentation is introduced, we will introduce some real scenarios applied in clinical practice. We can see that cardiac image processing has a large number of clinical problems are required to be solved. At the same time, we will briefly introduce the market situation of image processing in domestic market, integration of industry, education and research, and the main relevant policy trends. the development of the main related industries is introduced and involve in like 1) the establishment of related imaging databases in China, 2) the development of related imaging technologies in China, and 3) the development of related hardware equipment in China. At the end, it is discussed that the development of cardiac image segmentation processing is increasingly inseparable in related to the development of deep learning. However, because deep learning itself is difficult to be explained, we called on medical knowledge-interpreted method models, and deep learning based constraints are called to be resolved further, such homogeneity data sets and its related of higher accuracy.

Spatial normalization and quantification approaches of PET imaging for neurological disorders.

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for 18F-FDG, 11C-PIB, 18F-Florbetapir, 18F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.

Quantitative Comparisons of Deep-learning-based and Atlas-based Auto- segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma.

Manual segment target volumes were time-consuming and inter-observer variability couldn't be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem. To evaluate the accuracy and stability of Atlas-based and deep-learning-based auto-segmentation of the intermediate risk clinical target volume, composed of CTV2 and CTVnd, for nasopharyngeal carcinoma quantitatively. A cascade-deep-residual neural network was constructed to automatically segment CTV2 and CTVnd by deep learning method. Meanwhile, a commercially available software was used to automatically segment the same regions by Atlas-based method. The datasets included contrast computed tomography scans from 102 patients. For each patient, the two regions were manually delineated by one experienced physician. The similarity between the two auto-segmentation methods was quantitatively evaluated by Dice similarity coefficient, the 95th Hausdorff distance, volume overlap error and relative volume difference, respectively. Statistical analyses were performed using the ranked Wilcoxon test. The average Dice similarity coefficient (±standard deviation) given by the deep-learning- based and Atlas-based auto-segmentation were 0.84 (±0.03) and 0.74 (±0.04) for CTV2, 0.79 (±0.02) and 0.68 (±0.03) for CTVnd, respectively. For the 95th Hausdorff distance, the corresponding values were 6.30±3.55 mm and 9.34±3.39 mm for CTV2, 7.09±2.27 mm and 14.33±3.98 mm for CTVnd. Besides, volume overlap error and relative volume difference could also predict the same situations. Statistical analyses showed significant difference between the two auto-segmentation methods (p<0.01). Compared with the Atlas-based segmentation approach, the deep-learning-based segmentation method performed better both in accuracy and stability for meaningful anatomical areas other than organs at risk.

Comparison of state-of-the-art atlas-based bone segmentation approaches from brain MR images for MR-only radiation planning and PET/MR attenuation correction

Comparison of state-of-the-art atlas-based bone segmentation approaches from brain MR images for MR-only radiation planning and PET/MR attenuation correction

Atlas-based segmentation of temporal bone surface structures.

To develop a time-efficient automated segmentation approach that could identify surface structures on the temporal bone for use in surgical simulation software and preoperative surgical training. An atlas-based segmentation approach was developed to segment the tegmen, sigmoid sulcus, exterior auditory canal, interior auditory canal, and posterior canal wall in normal temporal bone CT images. This approach was tested in images of 20 cadaver bones (10 left, 10 right). The results of the automated segmentation were compared to manual segmentation using quantitative metrics of similarity, Mahalanobis distance, average Hausdorff distance, and volume similarity. The Mahalanobis distance was less than 0.232mm for all structures. The average Hausdorff distance was less than 0.464mm for all structures except the posterior canal wall and external auditory canal for the right bones. Volume similarity was 0.80 or greater for all structures except the sigmoid sulcus that was 0.75 for both left and right bones. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer. An atlas-based approach using a deformable registration of a Gaussian-smoothed temporal bone image and refinements using surface landmarks was successful in segmenting surface structures of temporal bone anatomy for use in pre-surgical planning and training.

Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.

The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intracranial electrodes, surgeons require accurate registration of the post-implantation CT images to the pre-implantation functional and structural magnetic resonance imaging to guide surgical resection of epileptic tissue. One way to perform the registration is via surface matching. The key challenge in this setup is the CT segmentation, where the extraction of the cortical surface is difficult due to the missing parts of the skull and artifacts introduced from the electrodes. In this paper, we present a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. We propose learning a model of locally oriented appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Utilizing a database of clinical epilepsy imaging data to train and test our approach, we demonstrate that our method using locally oriented image appearance both more accurately extracts the brain surface and better localizes electrodes on the post-operative brain surface compared to standard, non-oriented appearance modeling. In addition, we compare our method to a standard atlas-based segmentation approach and to a U-Net-based deep convolutional neural network segmentation method.

Selection Strategies for Atlas-Based Mosaicing of Left Atrial 3-D Transesophageal Echocardiography Data

Three-dimensional transesophageal echocardiography (TEE) provides real-time soft tissue information, but its use is hampered by its limited field of view. The mosaicing of multiple TEE views makes it possible to visualize a large structure, like the left atrium, in a single volume. To this end, an automatic registration method is required. Similarly to atlas-based segmentation approaches, atlas-based mosaicing (ABM) uses a full volume atlas set to moderate the onerous registration of the individual TEE views. The performance of ABM depends both on the quality of the involved registrations and on the selection of the optimal transformation from the candidate transformations that result from the various atlases. The study described here explored the performance of different selection strategies on multiview TEE data of the left atrium. We found that by incorporating two stages of transformation selection, using the image similarity and the conformity between the candidate transformations as selection criteria, the average registration error dropped below 3 mm with respect to manual registration of these data. Finally, we used this method for the automatic construction of a wide-view TEE volume of the left atrium.

Atlas-Based Segmentation of Temporal Bone Anatomy.

To develop a time-efficient automated segmentation approach that could identify critical structures in the temporal bone for visual enhancement and use in surgical simulation software. An atlas-based segmentation approach was developed to segment the cochlea, ossicles, semicircular canals (SCCs), and facial nerve in normal temporal bone CT images. This approach was tested in images of 26 cadaver bones (13 left, 13 right). The results of the automated segmentation were compared to manual segmentation visually and using DICE metric, average Hausdorff distance, and volume similarity. The DICE metrics were greater than 0.8 for the cochlea, malleus, incus, and the SCCs combined. It was slightly lower for the facial nerve. The average Hausdorff distance was less than one voxel for all structures, and the volume similarity was 0.86 or greater for all structures except the stapes. The atlas-based approach with rigid body registration of the otic capsule was successful in segmenting critical structures of temporal bone anatomy for use in surgical simulation software.

Atlas-Based Mosaicing of Left Atrial 3-D Transesophageal Echocardiography Images

Transesophageal echocardiography (TEE) is a promising imaging modality used to guide cardiac interventions, such as catheter ablation for the treatment of cardiac arrhythmias. These procedures rely on good visualization of the left atrium and pulmonary veins. To visualize these structures in a single volume, the acquisition, registration and fusion of multiple TEE views of the left atrium are required. We introduce atlas-based mosaicing as a method for the registration of images that are acquired according to a standardized protocol. Inspired by atlas-based segmentation approaches, compounded data of other patients serve as atlases for the registration of new data. The performance of atlas-based mosaicing is studied on 3-D TEE data of the left atrium and compared with that of regular pairwise registration. This study indicates that improved registration robustness and smaller registration errors are achieved with atlas-based mosaicing compared with regular pairwise registration. This is an important step toward the use of TEE for interventional guidance of ablation procedures.

Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools

In recent years, with the deepening of brain science research, mouse magnetic resonance microscopy (MRM) for neuroimaging research has gradually become a main research interest. Brain segmentation is an essential techniques for investigating the brain morphometry, while the traditional approach to segment a given brain involves the manual delineation of the ROIs by an expert. This practice can be slow and unscalable. Although automatic atlas-based segmentation approaches have been developed and validated for the human brain MRI, there is limited work for the mouse brain MRM. This paper combined optimized image registration and multi-atlas model for mouse brain segmentation. The results showed that multiple atlases with optimized geodesic-SyN can best improve the segmentation accuracy in the mouse brain, and registration algorithm plays important role in performance improvement.

Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8mm). Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences.

Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches.

Atlas-based segmentation is a powerful method for automatic structural segmentation of several sub-structures in many organs. However, such an approach has been very scarcely used in the context of muscle segmentation, and so far no study has assessed such a method for the automatic delineation of individual muscles of the quadriceps femoris (QF). In the present study, we have evaluated a fully automated multi-atlas method and a semi-automated single-atlas method for the segmentation and volume quantification of the four muscles of the QF and for the QF as a whole. The study was conducted in 32 young healthy males, using high-resolution magnetic resonance images (MRI) of the thigh. The multi-atlas-based segmentation method was conducted in 25 subjects. Different non-linear registration approaches based on free-form deformable (FFD) and symmetric diffeomorphic normalization algorithms (SyN) were assessed. Optimal parameters of two fusion methods, i.e., STAPLE and STEPS, were determined on the basis of the highest Dice similarity index (DSI) considering manual segmentation (MSeg) as the ground truth. Validation and reproducibility of this pipeline were determined using another MRI dataset recorded in seven healthy male subjects on the basis of additional metrics such as the muscle volume similarity values, intraclass coefficient, and coefficient of variation. Both non-linear registration methods (FFD and SyN) were also evaluated as part of a single-atlas strategy in order to assess longitudinal muscle volume measurements. The multi- and the single-atlas approaches were compared for the segmentation and the volume quantification of the four muscles of the QF and for the QF as a whole. Considering each muscle of the QF, the DSI of the multi-atlas-based approach was high 0.87±0.11 and the best results were obtained with the combination of two deformation fields resulting from the SyN registration method and the STEPS fusion algorithm. The optimal variables for FFD and SyN registration methods were four templates and a kernel standard deviation ranging between 5 and 8. The segmentation process using a single-atlas-based method was more robust with DSI values higher than 0.9. From the vantage of muscle volume measurements, the multi-atlas-based strategy provided acceptable results regarding the QF muscle as a whole but highly variable results regarding individual muscle. On the contrary, the performance of the single-atlas-based pipeline for individual muscles was highly comparable to the MSeg, thereby indicating that this method would be adequate for longitudinal tracking of muscle volume changes in healthy subjects. In the present study, we demonstrated that both multi-atlas and single-atlas approaches were relevant for the segmentation of individual muscles of the QF in healthy subjects. Considering muscle volume measurements, the single-atlas method provided promising perspectives regarding longitudinal quantification of individual muscle volumes.

Low‐complexity atlas‐based prostate segmentation by combining global, regional, and local metrics

To improve the efficiency of atlas-based segmentation without compromising accuracy, and to demonstrate the validity of the proposed method on MRI-based prostate segmentation application. Accurate and efficient automatic structure segmentation is an important task in medical image processing. Atlas-based methods, as the state-of-the-art, provide good segmentation at the cost of a large number of computationally intensive nonrigid registrations, for anatomical sites/structures that are subject to deformation. In this study, the authors propose to utilize a combination of global, regional, and local metrics to improve the accuracy yet significantly reduce the number of required nonrigid registrations. The authors first perform an affine registration to minimize the global mean squared error (gMSE) to coarsely align each atlas image to the target. Subsequently, a target-specific regional MSE (rMSE), demonstrated to be a good surrogate for dice similarity coefficient (DSC), is used to select a relevant subset from the training atlas. Only within this subset are nonrigid registrations performed between the training images and the target image, to minimize a weighted combination of gMSE and rMSE. Finally, structure labels are propagated from the selected training samples to the target via the estimated deformation fields, and label fusion is performed based on a weighted combination of rMSE and local MSE (lMSE) discrepancy, with proper total-variation-based spatial regularization. The proposed method was applied to a public database of 30 prostate MR images with expert-segmented structures. The authors' method, utilizing only eight nonrigid registrations, achieved a performance with a median/mean DSC of over 0.87/0.86, outperforming the state-of-the-art full-fledged atlas-based segmentation approach of which the median/mean DSC was 0.84/0.82 when applying to their data set. The proposed method requires a fixed number of nonrigid registrations, independent of atlas size, providing desirable scalability especially important for a large or growing atlas. When applied to prostate segmentation, the method achieved better performance to the state-of-the-art atlas-based approaches, with significant improvement in computation efficiency. The proposed rationale of utilizing jointly global, regional, and local metrics, based on the information characteristic and surrogate behavior for registration and fusion subtasks, can be extended naturally to similarity metrics beyond MSE, such as correlation or mutual information types.

A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results.

Imaging brain fibrillar β-amyloid in adults with Down syndrome (DS) using [11C] PiB-PET: acceptability, feasibility and preliminary results

age-related changes in the hippocampal shapes of healthy subjects across lifespan. Methods: We automatically delineate hippocampi from 302 MRI scans of healthy subjects (18-94 years; 114 males and 188 females) and apply LDDMM to find the optimal initial momentum that describes the hippocampal shape, referred as shape descriptor. We then apply LLDME to embed the high-dimensional hippocampal shape descriptor to a low dimensional space whose first few dimensions characterize the most of age-related variations of the hippocampal shape. Line arregression is performed with linear, quadratic, cubic terms of age as main factors and the embedding dimensions of hippocampal shapes as dependent variable. Hippocampi are first segmented from MRI scans by atlas-based segmentation approach, then hippocampal surface sare registered to a common template hippocampal surface through large diffeomorphic metric surface-based mapping LDDMM. Each hippocampal surfaceis then represented by the initial momentum computed from LDDMM, which encodes full information needed to transform the template to the target surface.Dimensionality of the high dimensional shape space is reduced by the locally linear diffeomorphic metric embedding (LLDME) approach that we developed recently in order to map anatomical variations onto low dimensional space for visualization and analysis. After projection, statistical association between low dimensional embedding and age is explored by regression analysis using polynomial model. Aging groups are also classified based on the low dimensional embeddings, and leave-one-out approach is used to estimate the classification error. Results: The shape descriptor of the hippocampus is projected to a low dimensional space whose first dimension is highly correlated with age (r 1⁄4 0.76, p < 0.0001). Figure 1 shows its distribution with respect to age. When it is fitted with age, a third-order polynomial model gives the best fit (R1⁄4 0.66), suggesting age-related processes of the hippocampal shape are non-linear throughout the lifespan with no/slow change before 50s, but accelerated change after 50s. In contrast, such nonlinear age-related processes are not clearly apparent in the hippocampal volume normalized by the total brain volume (Figure 2). The linear (green line) and quadratic fittings (redline) of the normalized hippocampal volume are very close to each other with R 1⁄4 0.014and R 1⁄4 0.03. Conclusions: Nonlinear age-related changes of hippocampal morphology are similar to those of whole brain. Such nonlinear relation is present in terms of the local hippocampal shape but not obvious in its size.

Atlas‐based segmentation of developing tissues in the human brain with quantitative validation in young fetuses

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 gray 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.

Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation

Neonatal brain MRI segmentation is a challenging problem due to its poor image quality. Atlas-based segmentation approaches have been widely used for guiding brain tissue segmentation. Existing brain atlases are usually constructed by equally averaging pre-segmented images in a population. However, such approaches diminish local inter-subject structural variability and thus lead to lower segmentation guidance capability. To deal with this problem, we propose a multi-region-multi-reference framework for atlas-based neonatal brain segmentation. For each region of a brain parcellation, a population of spatially normalized pre-segmented images is clustered into a number of sub-populations. Each sub-population of a region represents an independent distribution from which a regional probability atlas can be generated. A selection of these regional atlases, across different sub-regions, will in the end be adaptively combined to form an overall atlas specific to the query image. Given a query image, the determination of the appropriate set of regional atlases is achieved by comparing the query image regionally with the reference, or exemplar, of each sub-population. Upon obtaining an overall atlas, an atlas-based joint registration–segmentation strategy is employed to segment the query image. Since the proposed method generates an atlas which is significant more similar to the query image than the traditional average-shape atlas, better tissue segmentation results can be expected. This is validated by applying the proposed method on a large set of neonatal brain images available in our institute. Experimental results on a randomly selected set of 10 neonatal brain images indicate that the proposed method achieves higher tissue overlap rates and lower standard deviations (SDs) in comparison with manual segmentations, i.e., 0.86 (SD 0.02) for GM, 0.83 (SD 0.03) for WM, and 0.80 (SD 0.05) for CSF. The proposed method also outperforms two other average-shape atlas-based segmentation methods.

Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans

A novel atlas-based segmentation approach based on the combination of multiple registrations is presented. Multiple atlases are registered to a target image. To obtain a segmentation of the target, labels of the atlas images are propagated to it. The propagated labels are combined by spatially varying decision fusion weights. These weights are derived from local assessment of the registration success. Furthermore, an atlas selection procedure is proposed that is equivalent to sequential forward selection from statistical pattern recognition theory. The proposed method is compared to three existing atlas-based segmentation approaches, namely 1) single atlas-based segmentation, 2) average-shape atlas-based segmentation, and 3) multi-atlas-based segmentation with averaging as decision fusion. These methods were tested on the segmentation of the heart and the aorta in computed tomography scans of the thorax. The results show that the proposed method outperforms other methods and yields results very close to those of an independent human observer. Moreover, the additional atlas selection step led to a faster segmentation at a comparable performance.

A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy

A comprehensive three-dimensional digital atlas database of the C57BL/6J mouse brain was developed based on magnetic resonance microscopy images acquired on a 17.6-T superconducting magnet. By using both manual tracing and an atlas-based semi-automatic segmentation approach, T2⁎-weighted magnetic resonance microscopy images of 10 adult male formalin-fixed, excised C57BL/6J mouse brains were segmented into 20 anatomical structures. These structures included the neocortex, hippocampus, amygdala, olfactory bulbs, basal forebrain and septum, caudate-putamen, globus pallidus, thalamus, hypothalamus, central gray, superior colliculi, inferior colliculi, the rest of midbrain, cerebellum, brainstem, corpus callosum/external capsule, internal capsule, anterior commissure, fimbria, and ventricles. The segmentation data were formatted and stored into a database containing three different atlas types: 10 single-specimen brain atlases, an average brain atlas and a probabilistic atlas. Additionally, quantitative group information, such as variations in structural volume, surface area, magnetic resonance microscopy image intensity and local geometry, were computed and stored as an integral part of the database. The database augments ongoing efforts with other high priority strains as defined by the Mouse Phenome Database focused on providing a quantitative framework for accurate mapping of functional, genetic and protein expression patterns acquired by a myriad of technologies and imaging modalities.