No abstract

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

This study investigates the effect of microglial activation on microglial glutamate transporters in vitro. Stimuli known to activate microglia and/or to be associated with pathological conditions with an impaired astroglial glutamate uptake were compared. Morphological changes and marked increases in ED1 antigen expression were found after 8-h incubation of rat microglia in 56 mM KCl, 1 ng/ml lipopolysaccharide (LPS), and 100 microM glutamate, as well as in acidic and basic conditions, showing that the cells were activated. Of the stimuli used, only LPS induced a significant release of the proinflammatory cytokines tumor necrosis factor-alpha (TNF-alpha) and interleukin-6 (IL-6), and was the only stimulus that increased microglial GLT-1 expression and glutamate uptake capacity after 12-h incubation. This effect was probably mediated by TNF-alpha, since this cytokine mimicked the effect of LPS. Furthermore, the effect of LPS was blocked by thalidomide, an inhibitor of TNF-alpha synthesis. Additionally, neutralizing antibodies against TNF-alpha also blocked the increase, indicating TNF-alpha as an inducer of GLT-1 expression in microglia. It was also found that preincubation with glutamate dose-dependently inhibited the microglial glutamate uptake. This could suggest different physiological functions for microglial and astroglial glutamate uptake and might indicate a reciprocal control of GLT-1 expression between microglia and astrocytes.

An iterative Bayesian reconstruction algorithm for limited view angle tomography, or ectomography, based on the three-dimensional total variation (TV) norm has been developed. The TV norm has been described in the literature as a method for reducing noise in two-dimensional images while preserving edges, without introducing ringing or edge artefacts. It has also been proposed as a 2D regularization function in Bayesian reconstruction, implemented in an expectation maximization algorithm (TV-EM). The TV-EM was developed for 2D single photon emission computed tomography imaging, and the algorithm is capable of smoothing noise while maintaining edges without introducing artefacts. The TV norm was extended from 2D to 3D and incorporated into an ordered subsets expectation maximization algorithm for limited view angle geometry. The algorithm, called TV3D-EM, was evaluated using a modelled point spread function and digital phantoms. Reconstructed images were compared with those reconstructed with the 2D filtered backprojection algorithm currently used in ectomography. Results show a substantial reduction in artefacts related to the limited view angle geometry, and noise levels were also improved. Perhaps most important, depth resolution was improved by at least 45%. In conclusion, the proposed algorithm has been shown to improve the perceived image quality.

A tomographic time-domain reconstruction algorithm for solving the inverse electromagnetic problem is described. The application we have in mind is dielectric breast cancer detection but the results are of general interest to the field of microwave tomography. Reconstructions have been made from experimental and numerically simulated data for objects of different sizes in order to investigate the relation between the spectral content of the illuminating pulse and the quality of the reconstructed image. We have found that the spectral content is crucial for a successful reconstruction. The work has further shown that when imaging objects with different scale lengths it is an advantage to use a multiple step procedure. Low frequency content in the pulse is used to image the large structures and the reconstruction process then proceed by using higher frequency data to resolve small scale lengths. Good agreement between the results obtained from experimental data and simulated data has been achieved.