The frontiers of bioimaging are currently being pushed toward the integration and correlation
of several modalities to tackle biomedical research questions holistically and across multiple …

Over the last few decades, deformable image registration (DIR) has gained popularity in
image‐guided radiation therapy for a number of applications, such as contour propagation …

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 …

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 …

Multi-atlas based segmentation methods have shown their effectiveness in brain regions-of-
interesting (ROIs) segmentation, by propagating labels from multiple atlases to a target …

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 …

Recently, machine learning is massively on the rise in medical applications providing the
ability to predict diseases, plan treatment and monitor progress. Still, the use in a clinical …

Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex
and risky treatment approach, which requires precise planning, prior to the administration of …

Label fusion is one of the key steps in multi-atlas based segmentation of structural magnetic
resonance (MR) images. Although a number of label fusion methods have been developed …