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

The rapid expansion of machine learning is offering a new wave of opportunities for nuclear
medicine. This paper reviews applications of machine learning for the study of attenuation …

Background Glioblastoma is the most aggressive brain tumor with poor prognosis. The
purpose of this study is to improve the tissue characterization of these highly heterogeneous …

This paper presents a review of deep learning (DL) in multi-organ segmentation. We
summarized the latest DL-based methods for medical image segmentation and applications …

In this study, we propose a synthetic CT (sCT) aided MRI-CT deformable image registration
for head and neck radiotherapy. An image synthesis network, cycle consistent generative …

This paper reviewed the machine learning-based studies for quantitative positron emission
tomography (PET). Specifically, we summarized the recent developments of machine …

This chapter presents a review of the recent advancements of the deep learning (DL)-based
medical image multi-organ segmentation methods. The latest network architecture designs …

This book provides a comprehensive introduction to current state-of-the-art auto-
segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk …

Decreasing administered activity directly reduces radiation exposure to patients and medical
staff, but meanwhile has adverse impacts on image quality and PET quantification accuracy …

The application of MRI significantly improves the accuracy and reliability of target
delineation for many disease sites in radiotherapy due to its superior soft tissue contrast as …