Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …

Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …

With the fast progress in information technologies and artificial intelligence (AI), smart
healthcare has gained considerable momentum. By using advanced technologies like AI …

Transformer-based models, capable of learning better global dependencies, have recently
demonstrated exceptional representation learning capabilities in computer vision and …

Background Executive cognitive functions, including working memory, cognitive flexibility,
and inhibition, are impaired in schizophrenia. Executive functions rely on coordinated …

Whole brain segmentation of fine-grained structures using deep learning (DL) is a very
challenging task since the number of anatomical labels is very high compared to the number …

Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging
(dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the …

Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution
computed tomography (CT) is a challenging topic, in part due to the limited memory provide …

Depression is associated with markers of accelerated aging, but it is unclear how this
relationship changes across the lifespan. We examined whether a brain-based measure of …

Background Anatomical connectivity between the thalamus and cortex, including the
prefrontal cortex (PFC), is abnormal in schizophrenia. Overlapping phenotypes, including …