Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …

The tremendous success of machine learning algorithms at image recognition tasks in
recent years intersects with a time of dramatically increased use of electronic medical …

Purpose Accurate and timely organs‐at‐risk (OARs) segmentation is key to efficient and
high‐quality radiation therapy planning. The purpose of this work is to develop a deep …

Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …

Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …