Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic
procedures. However, the diagnosis by ultrasound relies heavily on the quality of images …

Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep
neural networks greatly assists in evaluating and planning treatments for various clinical …

Visual training has emerged as a useful framework for investigating training-related brain
plasticity, a highly complex task involving the interaction of visual orientation, attention …

Automated segmentation of cancerous lesions in PET/CT images is a vital initial task for
quantitative analysis. However, it is often challenging to train deep learning-based …

Objective. Training neural networks for pixel-wise or voxel-wise image segmentation is a
challenging task that requires a considerable amount of training samples with highly …

Automatic segmentation of breast cancer on dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI), which reveals both temporal and spatial profiles of the …

In medical image analysis, the expertise scarcity and the high cost of data annotation limits
the development of large artificial intelligence models. This paper investigates the potential …

Purpose: This study aimed to employ the incremental digital image correlation (DIC) method
to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences …

In addressing the issues of blurred microcrack edge gradients and mutual interference
among various types of microcracks in the semantic segmentation of microcracks in silicon …

Accurate identification of lesions is a key step in surgical planning. However, this task mainly
exists two challenges: 1) Due to the complex anatomical shapes of different lesions, most …