Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …

Deformable image registration can be time consuming and often needs extensive
parameterization to perform well on a specific application. We present a deformable …

Purpose To develop an accurate and fast deformable image registration (DIR) method for
four‐dimensional computed tomography (4D‐CT) lung images. Deep learning‐based …

Deformable image registration is an important tool in medical image analysis. In the case of
lung computed tomography (CT) registration there are three major challenges: large motion …

Purpose Automatic segmentation of organs‐at‐risk (OAR s) is a key step in radiation
treatment planning to reduce human efforts and bias. Deep convolutional neural networks …

Deformable image registration can be time-consuming and often needs extensive
parameterization to perform well on a specific application. We present a step towards a …

Aim. Alzheimer's disease (AD) is a chronic neurodegenerative disease. Recently, computer
scientists have developed various methods for early detection based on computer vision …

Landmark point-pairs provide a strategy to assess deformable image registration (DIR)
accuracy in terms of the spatial registration of the underlying anatomy depicted in medical …

Background: Within the past decade, computer scientists have developed many methods
using computer vision and machine learning techniques to detect Alzheimer's disease (AD) …

Radiation therapy (RT) of the lung requires deformation analysis. Deformable image
registration (DIR) is the fundamental method to quantify deformations for various …