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Scherrer B.,Computational Radiology Laboratory
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention | Year: 2011

Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white-matter but suffers from a relatively poor resolution. In this work we propose a super-resolution reconstruction (SRR) technique based on the acquisition of multiple anisotropic orthogonal DWI scans. We address the problem of patient motions by aligning the volumes both in space and in q-space. The SRR is formulated as a maximum a posteriori (MAP) problem. It relies on a volume acquisition model which describes the generation of the acquired scans from the unknown high-resolution image. It enables the introduction of image priors that exploit spatial homogeneity and enables regularized solutions. We detail our resulting SRR optimization procedure and report various experiments including numerical simulations, synthetic SRR scenario and real world SRR scenario. Super-resolution reconstruction in DWI may enable DWI to be performed with unprecedented resolution. Source


Akhondi-Asl A.,Computational Radiology Laboratory
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention | Year: 2012

We present a new fusion algorithm for the segmentation and parcellation of magnetic resonance (MR) images of the brain. Our algorithm is a parametric empirical Bayesian extension of the STAPLE algorithm which uses the observations to accurately estimate the prior distribution of the hidden ground truth using an expectation maximization (EM) algorithm. We use IBSR dataset for the evaluation of our fusion algorithm. We segment 128 principle gray and white matter structures of the brain using our novel method and eight other state-of-the-art algorithms in the literature. Our prior distribution estimation strategy improves the accuracy of the fusion algorithm. It was shown that our new fusion algorithm has superior performance compared to the other state-of-the-art fusion methods in the literature. Source


Akhondi-Asl A.,Computational Radiology Laboratory | Hoyte L.,University of South Florida | Lockhart M.E.,University of Alabama at Birmingham | Warfield S.K.,Computational Radiology Laboratory
IEEE Transactions on Medical Imaging | Year: 2014

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. © 1982-2012 IEEE. Source


Tomas-Fernandez X.,Computational Radiology Laboratory | Warfield S.K.,Computational Radiology Laboratory
IEEE Transactions on Medical Imaging | Year: 2015

White matter (WM) lesions are thought to play an important role in multiple sclerosis (MS) disease burden. Recent work in the automated segmentation of white matter lesions from magnetic resonance imaging has utilized a model in which lesions are outliers in the distribution of tissue signal intensities across the entire brain of each patient. However, the sensitivity and specificity of lesion detection and segmentation with these approaches have been inadequate. In our analysis, we determined this is due to the substantial overlap between the whole brain signal intensity distribution of lesions and normal tissue. Inspired by the ability of experts to detect lesions based on their local signal intensity characteristics, we propose a new algorithm that achieves lesion and brain tissue segmentation through simultaneous estimation of a spatially global within-the-subject intensity distribution and a spatially local intensity distribution derived from a healthy reference population. We demonstrate that MS lesions can be segmented as outliers from this intensity model of population and subject. We carried out extensive experiments with both synthetic and clinical data, and compared the performance of our new algorithm to those of state-of-the art techniques. We found this new approach leads to a substantial improvement in the sensitivity and specificity of lesion detection and segmentation. © 2015 IEEE. Source


Commowick O.,Computational Radiology Laboratory | Warfield S.K.,Computational Radiology Laboratory
IEEE Transactions on Medical Imaging | Year: 2010

The evaluation of the quality of segmentations of an image, and the assessment of intra-and inter-expert variability in segmentation performance, has long been recognized as a difficult task. For a segmentation validation task, it may be effective to compare the results of an automatic segmentation algorithm to multiple expert segmentations. Recently an expectation-maximization (EM) algorithm for simultaneous truth and performance level estimation (STAPLE) was developed to this end to compute both an estimate of the reference standard segmentation and performance parameters from a set of segmentations of an image. The performance is characterized by the rate of detection of each segmentation label by each expert in comparison to the estimated reference standard. © 2010 IEEE. Source

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