MRI-based medical image analysis for brain tumor studies is gaining attention in recent
times due to an increased need for efficient and objective evaluation of large amounts of …

We propose a framework for the robust and fully-automatic segmentation of magnetic
resonance (MR) brain images called “Multi-Atlas Label Propagation with Expectation …

Purpose: One of the important challenges in the field of medical imaging is finding real
clinical images with which to validate new image processing algorithms. This is particularly …

Registration of pre-operative and post-recurrence brain images is often needed to evaluate
the effectiveness of brain gliomas treatment. While recent deep learning-based deformable …

In this work, we present a novel algorithm for registration of 3-D volumetric ultrasound (US)
and MR using Robust PaTch-based cOrrelation Ratio (RaPTOR). RaPTOR computes local …

This paper examines the problem of diffeomorphic image registration in the presence of
differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our …

We present a common framework, for registering images to an atlas and for forming an
unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic …

We propose a new method for deformable registration of pre-operative and post-recurrence
brain MR scans of glioma patients. Performing this type of intra-subject registration is …

In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to
diseased patient registration framework. Both segmentation and registration problems are …

Medical image registration is a critical task that estimates the spatial correspondence
between pairs of images. However, current traditional and learning-based methods rely on …