OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images

Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Statistical normalization techniques for magnetic resonance imaging☆☆☆

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

African Ancestry and Its Correlation to Type 2 Diabetes in African Americans: A Genetic Admixture Analysis in Three U.S. Population Cohorts

The risk of type 2 diabetes is approximately 2-fold higher in African Americans than in European Americans even after adjusting for known environmental risk factors, including socioeconomic status (SES), suggesting that genetic factors may explain some of this population difference in disease risk. However, relatively few genetic studies have examined this hypothesis in a large sample of African Americans with and without diabetes. Therefore, we performed an admixture analysis using 2,189 ancestry-informative markers in 7,021 African Americans (2,373 with type 2 diabetes and 4,648 without) from the Atherosclerosis Risk in Communities Study, the Jackson Heart Study, and the Multiethnic Cohort to 1) determine the association of type 2 diabetes and its related quantitative traits with African ancestry controlling for measures of SES and 2) identify genetic loci for type 2 diabetes through a genome-wide admixture mapping scan. The median percentage of African ancestry of diabetic participants was slightly greater than that of non-diabetic participants (study-adjusted difference = 1.6%, P<0.001). The odds ratio for diabetes comparing participants in the highest vs. lowest tertile of African ancestry was 1.33 (95% confidence interval 1.13–1.55), after adjustment for age, sex, study, body mass index (BMI), and SES. Admixture scans identified two potential loci for diabetes at 12p13.31 (LOD = 4.0) and 13q14.3 (Z score = 4.5, P = 6.6×10−6). In conclusion, genetic ancestry has a significant association with type 2 diabetes above and beyond its association with non-genetic risk factors for type 2 diabetes in African Americans, but no single gene with a major effect is sufficient to explain a large portion of the observed population difference in risk of diabetes. There undoubtedly is a complex interplay among specific genetic loci and non-genetic factors, which may both be associated with overall admixture, leading to the observed ethnic differences in diabetes risk

AMI observations of Lynds Dark Nebulae: further evidence for anomalous cm-wave emission

Observations at 14.2 to 17.9 GHz made with the AMI Small Array towards fourteen Lynds Dark Nebulae with a resolution of 2' are reported. These sources are selected from the SCUBA observations of Visser et al. (2001) as small angular diameter clouds well matched to the synthesized beam of the AMI Small Array. Comparison of the AMI observations with radio observations at lower frequencies with matched uv-plane coverage is made, in order to search for any anomalous excess emission which can be attributed to spinning dust. Possible emission from spinning dust is identified as a source within a 2' radius of the Scuba position of the Lynds dark nebula, exhibiting an excess with respect to lower frequency radio emission. We find five sources which show a possible spinning dust component in their spectra. These sources have rising spectral indices in the frequency range 14.2--17.9 GHz. Of these five one has already been reported, L1111, we report one new definite detection, L675, and three new probable detections (L944, L1103 and L1246). The relative certainty of these detections is assessed on the basis of three criteria: the extent of the emission, the coincidence of the emission with the Scuba position and the likelihood of alternative explanations for the excess. Extended microwave emission makes the likelihood of the anomalous emission arising as a consequence of a radio counterpart to a protostar or a proto-planetary disk unlikely. We use a 2' radius in order to be consistent with the IRAS identifications of dark nebulae (Parker 1988), and our third criterion is used in the case of L1103 where a high flux density at 850 microns relative to the FIR data suggests a more complicated emission spectrum.Comment: submitted MNRA

G64.5+0.9, a new shell supernova remnant with unusual central emission

We present observations between 1.4 and 18 GHz confirming that G64.5+0.9 is new Galactic shell supernova remnant, using the Very Large Array and the Arcminute Microkelvin Imager. The remnant is a shell ~8 arcmin in diameter with a spectral index of alpha = 0.47 +/- 0.03 (with alpha defined such that flux density S varies with frequency nu as S proportional to nu to the power of -alpha). There is also emission near the centre of the shell, ~1 arcmin in extent, with a spectral index of alpha = 0.81 +/- 0.02. We do not find any evidence for spectral breaks for either source within our frequency range. The nature of the central object is unclear and requires further investigation, but we argue that is most unlikely to be extragalactic. It is difficult to avoid the conclusion that it is associated with the shell, although its spectrum is very unlike that of known pulsar wind nebulae.Comment: 5 pages, 6 figures, submitted to MNRA

The RAST Server: Rapid Annotations using Subsystems Technology

<p>Abstract</p> <p>Background</p> <p>The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them.</p> <p>Description</p> <p>We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment.</p> <p>The service normally makes the annotated genome available within 12–24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service.</p> <p>Conclusion</p> <p>By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.</p