Abstract Positron Emission Tomography (PET), a non-invasive functional imaging method at
the molecular level, images the distribution of biologically targeted radiotracers with high …

Radiomics, the high-throughput extraction of quantitative imaging features from medical
images, holds immense potential for advancing precision medicine in oncology and beyond …

Multimodality medical imaging techniques have been increasingly applied in clinical
practice and research studies. Corresponding multimodal image analysis and ensemble …

Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …

The analysis of multi-modality positron emission tomography and computed tomography
(PET-CT) images for computer-aided diagnosis applications (eg, detection and …

Positron emission tomography (PET) is a typical nuclear imaging technique, which can
provide crucial functional information for early brain disease diagnosis. Generally, clinically …

Automatic tumor segmentation from medical images is an important step for computer-aided
cancer diagnosis and treatment. Recently, deep learning has been successfully applied to …

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely
in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of …

Purpose We propose a single network trained by pixel‐to‐label deep learning to address
the general issue of automatic multiple organ segmentation in three‐dimensional (3D) …

Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously
acquire functional metabolic information and anatomical information of the human body …