registration

Pediatric Airway Analysis

Approaches to quantify pediatric airways.

A Deep Network for Joint Registration and Reconstruction of Images with Pathologies

Registration of images with pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. Moreover, mass effects as observed for brain tumors may displace tissue, creating larger deformations over …

Adversarial Data Augmentation via Deformation Statistics

Deep learning models have been successful in computer vision and medical image analysis. However, training these models frequently requires large labeled image sets whose creation is often very time and labor intensive, for example, in the context of …

Anatomical Data Augmentation via Fluid-Based Image Registration

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our …

Fluid Registration Between Lung CT and Stationary Chest Tomosynthesis Images

Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. However, while high-quality computed tomography (CT) images are often …

Registration of Images with Pathologies

Registration is one of the fundamental tasks in medical image analysis. It is an essential step for many applications to establish spatial correspondences between two images. However, image registration in the presence of pathologies is challenging …

Votenet+: An Improved Deep Learning Label Fusion Method for Multi-Atlas Segmentation

In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network …

DeepAtlas: Joint Semi-supervised Learning of Image Registration and Segmentation

Deep convolutional neural networks (CNNs) are state-of-theart for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. …

Fast predictive simple geodesic regression

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is …

Metric Learning for Image Registration

Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for …