Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US)
fetal images. A number of survey papers in the field is today available, but most of them are …

The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Knee segmentation and landmark localization from 3D MRI are two significant tasks for
diagnosis and treatment of knee diseases. With the development of deep learning …

The need for time and attention given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …

We present the first system that provides real-time probe movement guidance for acquiring
standard planes in routine freehand obstetric ultrasound scanning. Such a system can …

Automatic biomedical image segmentation plays an important role in speeding up disease
detection and diagnosis. The rapid development of Deep Learning has shown ground …

Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks
from cone-beam computed tomography (CBCT) images. However, due to the complexity of …

Biometric measurements in fetal ultrasound images are one of the most highly demanding
medical image analysis tasks that can directly contribute to diagnosing fetal diseases …

Deep learning is considered the leading artificial intelligence tool in image analysis in
general. Deep‐learning algorithms excel at image recognition, which makes them valuable …