Validation of tissue modelization and classification techniques in T1-weighted MR brain images

We propose a deep study on tissue modelization andclassification Techniques on T1-weighted MR images. Threeapproaches have been taken into account to perform thisvalidation study. Two of them are based on FiniteGaussian Mixture (FGM) model. The first one consists onlyin pure gaussian distributions (FGM-EM). The second oneuses a different model for partial volume (PV) (FGM-GA).The third one is based on a Hidden Markov Random Field(HMRF) model. All methods have been tested on a DigitalBrain Phantom image considered as the ground truth. Noiseand intensity non-uniformities have been added tosimulate real image conditions. Also the effect of ananisotropic filter is considered. Results demonstratethat methods relying in both intensity and spatialinformation are in general more robust to noise andinhomogeneities. However, in some cases there is nosignificant differences between all presented methods.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Cysteine-rich Domain 1 of CD40 Mediates Receptor Self-assembly.

The activation of CD40 on B cells, macrophages, and dendritic cells by its ligand CD154 (CD40L) is essential for the development of humoral and cellular immune responses. CD40L and other TNF superfamily ligands are noncovalent homotrimers, but the form under which CD40 exists in the absence of ligand remains to be elucidated. Here, we show that both cell surface-expressed and soluble CD40 self-assemble, most probably as noncovalent dimers. The cysteine-rich domain 1 (CRD1) of CD40 participated to dimerization and was also required for efficient receptor expression. Modelization of a CD40 dimer allowed the identification of lysine 29 in CRD1, whose mutation decreased CD40 self-interaction without affecting expression or response to ligand. When expressed alone, recombinant CD40-CRD1 bound CD40 with a KD of 0.6 μm. This molecule triggered expression of maturation markers on human dendritic cells and potentiated CD40L activity. These results suggest that CD40 self-assembly modulates signaling, possibly by maintaining the receptor in a quiescent state.