A key limitation of deep convolutional neural network (DCNN)-based image segmentation
methods is the lack of generalizability. Manually traced training images are typically required …

A lack of generalizability is one key limitation of deep learning based segmentation.
Typically, one manually labels new training images when segmenting organs in different …

Pathological brain detection is an automated computer-aided diagnosis for brain images.
This study provides a novel method to achieve this goal. We first used synthetic minority …

High-throughput, large-scale medical image computing demands tight integration of high-
performance computing (HPC) infrastructure for data storage, job distribution, and image …

Pathological brain detection by computer vision is now attracting intense attentions from
academic fields. Nevertheless, most of recent methods suffer from low-accuracy. This study …

Intracranial volume (ICV) is frequently used in volumetric magnetic resonance imaging (MRI)
studies, both as a covariate and as a variable of interest. Findings of associations between …

When analyzing large multicenter databases, the effects of multiple confounding covariates
increase the variability in the data and may reduce the ability to detect changes due to the …

Background Segmentation tools continue to advance, evolving from manual contouring to
deep learning. Researchers have utilized segmentation to study a myriad of posterior fossa …

Abstract (Aim) Alcohol use disorder may put health at risk and cause serious health
problems. It is of increasing importance to identify alcohol use disorder as early as …

Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord,
leading to a wide range of potential symptoms. This method aims to propose a novel MS …