Purpose of the review We aim to review the methods, current research evidence, and future
directions in body composition analysis (BCA) with CT imaging. Recent findings CT images …

Automated computed tomography (CT) scan segmentation (labelling of pixels according to
tissue type) is now possible. This technique is being adapted to achieve three‐dimensional …

Segmentation has been a rooted area of research having diverse dimensions. The roots of
image segmentation and its associated techniques have supported computer vision, pattern …

Background Personalized therapy planning remains a significant challenge in advanced
colorectal cancer care, despite extensive research on prognostic and predictive markers. A …

The present review summarizes the available evidence on artificial intelligence (AI)
algorithms aimed to the segmentation of epicardial and pericardial adipose tissues on …

Body composition imaging relies on assessment of tissues composition and distribution.
Quantitative data provided by body composition imaging analysis have been linked to …

Purpose Tracing muscle groups manually on CT to calculate body composition parameters
and diagnose sarcopenia is costly and time consuming. Artificial Intelligence (AI) provides …

Background Computed tomography (CT) scans are routinely obtained in oncology and
provide measures of muscle and adipose tissue predictive of morbidity and mortality …

Purpose The purpose of this article is to provide an up-to-date summary of sarcopenia and
its clinical implications for patients with head and neck cancer (HNC). Methods We …

The security of AI systems has gained significant attention in recent years, particularly in the
medical diagnosis field. To develop a secure medical image classification system based on …