3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

Median Filter Architecture by Accumulative Parallel Counters

The time to process each of the W/B processing blocks of a median calculation method on a set of N W-bit integers is improved here by a factor of three compared with literature. The parallelism uncovered in blocks containing B-bit slices is exploited by independent accumulative parallel counters so that the median is calculated faster than any known previous method for any N, W values. The improvements to the method are discussed in the context of calculating the median for a moving set of N integers, for which a pipelined architecture is developed. An extra benefit of a smaller area for the architecture is also reported.

Molecular mechanisms controlling synaptic recruitment of GluA4 subunit-containing AMPAreceptors

Synaptic recruitment of AMPA receptors (AMPARs) represents a key postsynaptic mechanism driving functional development and maturation of glutamatergic synapses. At immature hippocampal synapses, PKA-driven synaptic insertion of GluA4 is the predominant mechanism for synaptic reinforcement. However, the physiological significance and molecular determinants of this developmentally restricted form of plasticity are not known. Here we show that PKA activation leads to insertion of GluA4 to synaptic sites with initially weak or silent AMPAR-mediated transmission. This effect depends on a novel mechanism involving the extreme C-terminal end of GluA4, which interacts with the membrane proximal region of the C-terminal domain to control GluA4 trafficking. In the absence of GluA4, strengthening of AMPAR-mediated transmission during postnatal development was significantly delayed. These data suggest that the GluA4-mediated activation of silent synapses is a critical mechanism facilitating the functional maturation of glutamatergic circuitry during the critical period of experience-dependent fine-tuning.