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 …

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

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

The anatomy of cancer patients changes between radiation treatment planning and delivery
as well as over the course of radiotherapy. Adaptive radiotherapy (ART) aims to deliver …

Radiation therapy treatments are typically planned based on a single image set, assuming
that the patient's anatomy and its position relative to the delivery system remains constant …

It is recognized that the use of a single plan calculated on an image acquired some time
before the treatment is generally insufficient to accurately represent the daily dose to the …

In medicine, image registration is vital in image-guided interventions and other clinical
applications. However, it is a difficult subject to be addressed which by the advent of …

Dose mapping/accumulation (DMA) is a topic in radiotherapy (RT) for years, but has not yet
found its widespread way into clinical RT routine. During the ESTRO Physics workshop …

Abstract Purpose The POP-ART RT study aims to determine to what extent and how
intrafractional real-time respiratory motion management (RRMM), and plan adaptation for …

Background and purpose Non-small cell lung cancer (NSCLC) patients show typically large
anatomical changes during treatment, making recalculation or adaption necessary. For …