Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

Background: Over half a million individuals are diagnosed with head and neck cancer each
year globally. Radiotherapy is an important curative treatment for this disease, but it requires …

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …

Purpose Radiation therapy (RT) is a common treatment option for head and neck (HaN)
cancer. An important step involved in RT planning is the delineation of organs‐at‐risks …

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 …

Purpose Accurate segmentation of organs‐at‐risks (OAR s) is the key step for efficient
planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we …

Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …

Purpose Intensity modulated radiation therapy (IMRT) is commonly employed for treating
head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing …

Introduction Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN
radiotherapy and for investigating the relationships between radiation dose to OARs and …