This paper presents a variational level set approach in a multi-phase formulation to segmentation of brain magnetic resonance (MR) images with intensity inhomogeneity. In our model, the local image intensities are characterized by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with level set functions and local means and variances as variables. The means and variances of local intensities are considered as spatially varying functions. Therefore, our method is able to deal with intensity inhomogeneity without inhomogeneity correction. Our method has been applied to 3T and 7T MR images with promising results.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jneumeth.2010.03.004 | DOI Listing |
Alzheimers Dement
December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: The ability to monitor cognitive trajectories over the course of trials can provide valuable insights into treatment efficacy. However, existing trial methods are limited in monitoring cognition in real-time and at high frequencies. Gameplay-based assessments hold promise as complementary cognitive tools.
View Article and Find Full Text PDFBackground: Genetic studies have established that loss of function SORL1 gene variants are associated with Alzheimer's disease (AD). SORL1 encodes an endosomal trafficking receptor, SORLA, which regulates endosomal protein recycling through its interaction with the retromer core complex (consisting of VPS26, VPS35 and VPS29). Deficits in the levels and function of the SORLA-retromer complex are thought to underlie AD.
View Article and Find Full Text PDFBackground: Neuroinflammation is a critical factor of Alzheimer's Disease (AD). Dysregulation of complement leads to excessive inflammation, direct damage to self-cells and propagation of injury. This is likely of particular relevance in the brain where inflammation is poorly tolerated and brain cells are vulnerable to direct damage by complement.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
View Article and Find Full Text PDFBackground: Irsenontrine (e2027) is a potent and selective PDE9 inhibitor that increases cellular cGMP which is important for glutamatergic synaptic function. Irsenontrine was investigated to improve cognition in Lewy Body Dementia (LBD; DLB and PDD), and recent phase 2 study data suggests that irsenontrine could be more effective in DLB patients without amyloid copathology. Here, we evaluated differential change from baseline levels in proteins associated with cGMP pathway in DLB participants without amyloid co-pathology (DLB A-) compared to DLB participants with amyloid co-pathology (DLB A+).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!