Abstract Three-dimensional (3D) reconstruction of human organs has gained attention in
recent years due to advances in the Internet and graphics processing units. In the coming …

There are considerable interests in automatic stroke lesion segmentation on magnetic
resonance (MR) images in the medical imaging field, as stroke is an important …

As a non-invasive and radiation-free imaging technique, magnetic resonance imaging (MRI)
can intuitively display the three-dimensional tissues and structures of human brain, showing …

Background Artificial intelligence aided tumor segmentation has been applied in various
medical scenarios and showed effectiveness in helping physicians observe the potential …

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms
of diagnosis and management. The growing integration of AI-based algorithms in healthcare …

Automatic segmentation of medical images is very important for computer-aided diagnosis.
The U-shaped and skip-connection based on convolution (UNet) has achieved the most …

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis
and monitoring its progression. Although several attempts have been made to segment …

Automated segmentation of brain white matter lesions is crucial for both clinical assessment
and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an …

Accurate lung tumor identification is crucial for radiation treatment planning. Due to the low
contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor …

Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous
system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely …