Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …

Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …

Large-scale labeled data are generally required to train deep neural networks in order to
obtain better performance in visual feature learning from images or videos for computer …

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …

Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …

The FlowNet demonstrated that optical flow estimation can be cast as a learning problem.
However, the state of the art with regard to the quality of the flow has still been defined by …

State-of-the-art approaches for semantic image segmentation are built on Convolutional
Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a …

Standard video frame interpolation methods first estimate optical flow between input frames
and then synthesize an intermediate frame guided by motion. Recent approaches merge …

This paper introduces a network for volumetric segmentation that learns from sparsely
annotated volumetric images. We outline two attractive use cases of this method:(1) In a …

Segmentation of key brain tissues from 3D medical images is of great significance for brain
disease diagnosis, progression assessment and monitoring of neurologic conditions. While …