Alireza Akhondi-Asl

Web-based interactive 2D/3D medical image processing and visualization software

There are many medical image processing software tools available for research and diagnosis purposes. However, most of these tools are available only as local applications. This limits the accessibility of the software to a specific machine, and thus the data and processing power of that application are not available to other workstations. Further, there are operating system and processing power limitations which prevent such applications from running on every type of workstation. By developing web-based tools, it is possible for users to access the medical image processing functionalities wherever the internet is available. In this paper, we introduce a pure web-based, interactive, extendable, 2D and 3D medical image processing and visualization application that requires no client installation. Our software uses a four-layered design consisting of an algorithm layer, web-user-interface layer, server communication layer, and wrapper layer. To compete with extendibility of the current local medical image processing software, each layer is highly independent of other layers. A wide range of medical image preprocessing, registration, and segmentation methods are implemented using open source libraries. Desktop-like user interaction is provided by using AJAX technology in the web-user-interface. For the visualization functionality of the software, the VRML standard is used to provide 3D features over the web. Integration of these technologies has allowed implementation of our purely web-based software with high functionality without requiring powerful computational resources in the client side. The user-interface is designed such that the users can select appropriate parameters for practical research and clinical studies.

Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new maximum a posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the-art algorithms.

Hippocampal volumetry for lateralization of temporal lobe epilepsy: automated versus manual methods.

The hippocampus has been the primary region of interest in the preoperative imaging investigations of mesial temporal lobe epilepsy (mTLE). Hippocampal imaging and electroencephalographic features may be sufficient in several cases to declare the epileptogenic focus. In particular, hippocampal atrophy, as appreciated on T1-weighted (T1W) magnetic resonance (MR) images, may suggest a mesial temporal sclerosis. Qualitative visual assessment of hippocampal volume, however, is influenced by head position in the magnet and the amount of atrophy in different parts of the hippocampus. An entropy-based segmentation algorithm for subcortical brain structures (LocalInfo) was developed and supplemented by both a new multiple atlas strategy and a free-form deformation step to capture structural variability. Manually segmented T1-weighted magnetic resonance (MR) images of 10 non-epileptic subjects were used as atlases for the proposed automatic segmentation protocol which was applied to a cohort of 46 mTLE patients. The segmentation and lateralization accuracies of the proposed technique were compared with those of two other available programs, HAMMER and FreeSurfer, in addition to the manual method. The Dice coefficient for the proposed method was 11% (p<10(-5)) and 14% (p<10(-4)) higher in comparison with the HAMMER and FreeSurfer, respectively. Mean and Hausdorff distances in the proposed method were also 14% (p<0.2) and 26% (p<10(-3)) lower in comparison with HAMMER and 8% (p<0.8) and 48% (p<10(-5)) lower in comparison with FreeSurfer, respectively. LocalInfo proved to have higher concordance (87%) with the manual segmentation method than either HAMMER (85%) or FreeSurfer (83%). The accuracy of lateralization by volumetry in this study with LocalInfo was 74% compared to 78% with the manual segmentation method. LocalInfo yields a closer approximation to that of manual segmentation and may therefore prove to be more reliable than currently published automatic segmentation algorithms.

Multi-Atlas Multi-Shape Segmentation of Fetal Brain MRI for Volumetric and Morphometric Analysis of Ventriculomegaly

The recent development of motion robust super-resolution fetal brain MRI holds out the potential for dramatic new advances in volumetric and morphometric analysis. Volumetric analysis based on volumetric and morphometric biomarkers of the developing fetal brain must include segmentation. Automatic segmentation of fetal brain MRI is challenging, however, due to the highly variable size and shape of the developing brain; possible structural abnormalities; and the relatively poor resolution of fetal MRI scans. To overcome these limitations, we present a novel, constrained, multi-atlas, multi-shape automatic segmentation method that specifically addresses the challenge of segmenting multiple structures with similar intensity values in subjects with strong anatomic variability. Accordingly, we have applied this method to shape segmentation of normal, dilated, or fused lateral ventricles for quantitative analysis of ventriculomegaly (VM), which is a pivotal finding in the earliest stages of fetal brain development, and warrants further investigation. Utilizing these innovative techniques, we introduce novel volumetric and morphometric biomarkers of VM comparing these values to those that are generated by standard methods of VM analysis, i.e., by measuring the ventricular atrial diameter (AD) on manually selected sections of 2D ultrasound or 2D MRI. To this end, we studied 25 normal and abnormal fetuses in the gestation age (GA) range of 19 to 39 weeks (mean=28.26, stdev=6.56). This heterogeneous dataset was essentially used to 1) validate our segmentation method for normal and abnormal ventricles; and 2) show that the proposed biomarkers may provide improved detection of VM as compared to the AD measurement.

Simultaneous Truth and Performance Level Estimation Through Fusion of Probabilistic Segmentations

Recent research has demonstrated that improved image segmentation can be achieved by multiple template fusion utilizing both label and intensity information. However, intensity weighted fusion approaches use local intensity similarity as a surrogate measure of local template quality for predicting target segmentation and do not seek to characterize template performance. This limits both the usefulness and accuracy of these techniques. Our work here was motivated by the observation that the local intensity similarity is a poor surrogate measure for direct comparison of the template image with the true image target segmentation. Although the true image target segmentation is not available, a high quality estimate can be inferred, and this in turn allows a principled estimate to be made of the local quality of each template at contributing to the target segmentation. We developed a fusion algorithm that uses probabilistic segmentations of the target image to simultaneously infer a reference standard segmentation of the target image and the local quality of each probabilistic segmentation. The concept of comparing templates to a hidden reference standard segmentation enables accurate assessments of the contribution of each template to inferring the target image segmentation to be made, and in practice leads to excellent target image segmentation. We have used the new algorithm for the multiple-template-based segmentation and parcellation of magnetic resonance images of the brain. Intensity and label map images of each one of the aligned templates are used to train a local Gaussian mixture model based classifier. Then, each classifier is used to compute the probabilistic segmentations of the target image. Finally, the generated probabilistic segmentations are fused together using the new fusion algorithm to obtain the segmentation of the target image. We evaluated our method in comparison to other state-of-the-art segmentation methods. We demonstrated that our new fusion algorithm has higher segmentation performance than these methods.

A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights.

Pelvic floor dysfunction is common in women after childbirth and precise segmentation of magnetic resonance images (MRI) of the pelvic floor may facilitate diagnosis and treatment of patients. However, because of the complexity of its structures, manual segmentation of the pelvic floor is challenging and suffers from high inter and intra-rater variability of expert raters. Multiple template fusion algorithms are promising segmentation techniques for these types of applications, but they have been limited by imperfections in the alignment of templates to the target, and by template segmentation errors. A number of algorithms sought to improve segmentation performance by combining image intensities and template labels as two independent sources of information, carrying out fusion through local intensity weighted voting schemes. This class of approach is a form of linear opinion pooling, and achieves unsatisfactory performance for this application. We hypothesized that better decision fusion could be achieved by assessing the contribution of each template in comparison to a reference standard segmentation of the target image and developed a novel segmentation algorithm to enable automatic segmentation of MRI of the female pelvic floor. The algorithm achieves high performance by estimating and compensating for both imperfect registration of the templates to the target image and template segmentation inaccuracies. A local image similarity measure is used to infer a local reliability weight, which contributes to the fusion through a novel logarithmic opinion pooling. We evaluated our new algorithm in comparison to nine state-of-the-art segmentation methods and demonstrated our algorithm achieves the highest performance.

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth.

Three-dimensional hip cartilage quality assessment of morphology and dGEMRIC by planar maps and automated segmentation.

OBJECTIVE The quantitative interpretation of hip cartilage magnetic resonance imaging (MRI) has been limited by the difficulty of identifying and delineating the cartilage in a three-dimensional (3D) dataset, thereby reducing its routine usage. In this paper a solution is suggested by unfolding the cartilage to planar two-dimensional (2D) maps on which both morphology and biochemical degeneration patterns can be investigated across the entire hip joint. DESIGN Morphological TrueFISP and biochemical delayed gadolinium enhanced MRI of cartilage (dGEMRIC) hip images were acquired isotropically for 15 symptomatic subjects with mild or no radiographic osteoarthritis (OA). A multi-template based label fusion technique was used to automatically segment the cartilage tissue, followed by a geometric projection algorithm to generate the planar maps. The segmentation performance was investigated through a leave-one-out study, for two different fusion methods and as a function of the number of utilized templates. RESULTS For each of the generated planar maps, various patterns could be seen, indicating areas of healthy and degenerated cartilage. Dice coefficients for cartilage segmentation varied from 0.76 with four templates to 0.82 with 14 templates. Regional analysis suggests even higher segmentation performance in the superior half of the cartilage. CONCLUSIONS The proposed technique is the first of its kind to provide planar maps that enable straightforward quantitative assessment of hip cartilage morphology and dGEMRIC values. This technique may have important clinical applications for patient selection for hip preservation surgery, as well as for epidemiological studies of cartilage degeneration patterns. It is also shown that 10-15 templates are sufficient for accurate segmentation in this application.

Construction of a deformable spatiotemporal MRI atlas of the fetal brain: evaluation of similarity metrics and deformation models.

The development and identification of best methods in fetal brain MRI analysis is crucial as we expect an outburst of studies on groupwise and longitudinal analysis of early brain development in the upcoming years. To address this critical need, in this paper, we have developed a mathematical framework for the construction of an unbiased deformable spatiotemporal atlas of the fetal brain MRI and compared it to alternative configurations in terms of similarity metrics and deformation models. Our contributions are twofold: first we suggest a novel approach to fetal brain spatiotemporal atlas construction that shows high capability in capturing anatomic variation between subjects; and second, within our atlas construction framework we evaluate and compare a set of plausible configurations for inter-subject fetal brain MRI registration and identify the most accurate approach that can potentially lead to most accurate results in population atlas construction, atlas-based segmentation, and group analysis. Our evaluation results indicate that symmetric diffeomorphic deformable registration with cross correlation similarity metric outperforms other configurations in this application and results in sharp unbiased atlases that can be used in fetal brain MRI analysis.

Lateralization of temporal lobe epilepsy using a novel uncertainty analysis of MR diffusion in hippocampus, cingulum, and fornix, and hippocampal volume and FLAIR intensity

PURPOSE To analyze the utility of a quantitative uncertainty analysis approach for evaluation and comparison of various MRI findings for the lateralization of epileptogenicity in mesial temporal lobe epilepsy (mTLE), including novel diffusion-based analyses. METHODS We estimated the hemispheric variation uncertainty (HVU) of hippocampal T1 volumetry and FLAIR (Fluid Attenuated Inversion Recovery) intensity. Using diffusion tensor images of 23 nonepileptic subjects, we estimated the HVU levels of mean diffusivity (MD) in the hippocampus, and fractional anisotropy (FA) in the posteroinferior cingulum and crus of fornix. Imaging from a retrospective cohort of 20 TLE patients who had undergone surgical resection with Engel class I outcomes was analyzed to determine whether asymmetry of preoperative volumetrics, FLAIR intensities, and MD values in hippocampi, as well as FA values in posteroinferior cingula and fornix crura correctly predicted laterality of seizure onset. Ten of the cohort had pathologically proven mesial temporal sclerosis (MTS). Seven of these patients had undergone extraoperative electrocorticography (ECoG) for lateralization or to rule out extra-temporal foci. RESULTS HVU was estimated to be 3.1×10(-5) for hippocampal MD, 0.027 for FA in posteroinferior cingulum, 0.018 for FA in crus of fornix, 0.069 for hippocampal normalized volume, and 0.099 for hippocampal normalized FLAIR intensity. Using HVU analysis, a higher hippocampal MD value, lower FA within the posteroinferior cingulum and crus of fornix, shrinkage in hippocampal volume, and higher hippocampal FLAIR intensity were observed beyond uncertainty on the side ipsilateral to seizure onset for 10, 10, 9, 9, and 10 out of 10 pathology-proven MTS patients, respectively. Considering all 20 TLE patients, these numbers were 18, 15, 14, 13, and 16, respectively. However, consolidating the lateralization results of HVU analysis on these quantities by majority voting has detected the epileptogenic side for 19 out of 20 cases with no wrong lateralization. CONCLUSION The presence of MTS in TLE patients is associated with an elevated MD value in the ipsilateral hippocampus and a reduced FA value in the posteroinferior subregion of the ipsilateral cingulum and crus of ipsilateral fornix. When considering all TLE patients, among the mentioned biomarkers the hippocampal MD had the best performance with true detection rate of 90% without any wrong lateralization. The proposed uncertainty based analyses hold promise for improving decision-making for surgical resection.