Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Given that image registration is a fundamental and ubiquitous task in both clinical and
research domains of the medical field, errors in registration can have serious consequences …

Over the past decade, deep learning technologies have greatly advanced the field of
medical image registration. The initial developments, such as ResNet-based and U-Net …

Medical imaging (image acquisition, image transformation, and image visualization) is a
standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students …

Discrete optimisation strategies have a number of advantages over their continuous
counterparts for deformable registration of medical images. For example: it is not necessary …

Medical imaging is an indispensable component of modern healthcare, playing a critical role
in diagnosis, staging, and the assessment of treatment response for most major medical …

In this paper we propose a supervised method to predict registration misalignment using
convolutional neural networks (CNNs). This task is casted to a classification problem with …

Predicting registration error can be useful for evaluation of registration procedures, which is
important for the adoption of registration techniques in the clinic. In addition, quantitative …

In brain tumor resection, accurate removal of cancerous tissues while preserving eloquent
regions is crucial to the safety and outcomes of the treatment. However, intra-operative …

This paper reports a new automatic algorithm to estimate the misregistration in a quantitative
manner. A random regression forest is constructed, predicting the local registration error …