Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and
interventional medical image analysis. Current solutions for anatomy detection are typically …

Automatic liver segmentation in 3D medical images is essential in many clinical applications,
such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative …

3-D image registration, which involves aligning two or more images, is a critical step in a
variety of medical applications from diagnosis to therapy. Image registration is commonly …

Purpose To present a method that automatically segments and quantifies abnormal CT
patterns commonly present in COVID-19, namely ground-glass opacities and consolidations. …

Chest X-ray is the most common medical imaging exam used to assess multiple pathologies.
Automated algorithms and tools have the potential to support the reading workflow, …

While deep convolutional neural networks (CNN) have been successfully applied to 2D
image analysis, it is still challenging to apply them to 3D medical images, especially when the …

Purpose Building accurate and robust artificial intelligence systems for medical image
assessment requires the creation of large sets of annotated training examples. However, …

Prostate-specific membrane antigen (PSMA)–targeting PET imaging is becoming the
reference standard for prostate cancer staging, especially in advanced disease. Yet, the …

The interpretation of medical images is a challenging task, often complicated by the
presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest …

Chest radiography is the most common radiographic examination performed in daily clinical
practice for the detection of various heart and lung abnormalities. The large amount of data …