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 …

Introduction Automatic functional volume segmentation in PET images is a challenge that
has been addressed using a large array of methods. A major limitation for the field has been …

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

Prior learning is a fundamental problem in the field of image processing. In this paper, we
conduct a detailed study on (1) how to model and learn the prior of the image patch group, …

Positron emission tomography (PET) and computed tomography (CT) are widely used
imaging modalities in radiation oncology. PET imaging has a high contrast but often leads to …

Accurate tumor segmentation in PET is crucial in many oncology applications. We
developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC…

CT scan is an important reference means of disease diagnosis in practice. Automatic
segmentation of organ regions can save a lot of time and labor costs, and allow doctors to produce …

Objective. Over the past years, convolutional neural networks based methods have dominated
the field of medical image segmentation. But the main drawback of these methods is that …

Accurate tumor segmentation from PET images is crucial in many radiation oncology applications.
Among others, partial volume effect (PVE) is recognized as one of the most important …

For data analysis, learning treatment rules in stratified medicine require the optimization of
multiple responses. A common approach is to use a multi-objective function to find the optimal …