Deformable image registration - Open Access .click

Results 61 to 70 of about 31,416 (280)

High Health Care Utilization Preceding Diagnosis With Juvenile Idiopathic Arthritis

Arthritis Care &Research, EarlyView.
Objective Although early diagnosis improves long‐term outcomes, patients with juvenile idiopathic arthritis (JIA) often experience prolonged, circuitous paths to diagnosis. To inform diagnostic improvement, we sought to characterize health care utilization in the year preceding diagnosis. Methods We identified 10,021 patients with an incident diagnosis
Anna Costello +5 more
wiley +1 more source

Registration error of the liver CT using deformable image registration of MIM Maestro and Velocity AI

BMC Medical Imaging, 2017
Background Understanding the irradiated area and dose correctly is important for the reirradiation of organs that deform after irradiation, such as the liver.
Nobuyoshi Fukumitsu +11 more
doaj +1 more source

Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration

Applied Sciences, 2023
Multimodal medical image registration has an important role in monitoring tumor growth, radiotherapy, and disease diagnosis. Deep-learning-based methods have made great progress in the past few years.
Xin Zhang, Tiejun Yang, Xiang Zhao, Aolin Yang +3 more
doaj +1 more source

Cumulative Social Disadvantage and Disease Activity in Juvenile Idiopathic Arthritis: A Childhood Arthritis and Rheumatology Research Alliance Registry Study

Arthritis Care &Research, EarlyView.
Objective Social determinants of health (SDOH) contribute to juvenile idiopathic arthritis (JIA) disparities, but most studies have assessed SDOH independently rather than cumulatively across individual, family, and neighborhood levels. Using a socioecological framework, we investigated the relationship among cumulative social disadvantage ...
William Daniel Soulsby +448 more
wiley +1 more source

Multistep Networks for Deformable Multimodal Medical Image Registration

IEEE Access
We proposed neural networks for deformable multimodal medical image registration that use multiple steps and varying resolutions. The networks were trained jointly in an unsupervised manner with Mutual Information and Gradient L2 loss.
Anika Strittmatter, Frank G. Zollner
doaj +1 more source

An Unsupervised Learning Model for Deformable Medical Image Registration

, 2018
We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data.
Balakrishnan, Guha +4 more
core +1 more source

Deformable image registration for tissues with large displacements [PDF]

Journal of Medical Imaging, 2017
Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients' movements such as respiration and repositioning. In our previous work, we proposed a fast registration method for deformable tissues with small rotations.
Xishi, Huang +3 more
openaire +2 more sources

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source

Nonrigid Medical Image Registration by Finite-Element Deformable Sheet-Curve Models

International Journal of Biomedical Imaging, 2006
Image-based change quantitation has been recognized as a promising tool for accurate assessment of tumor's early response to chemoprevention in cancer research.
Jianhua Xuan +4 more
doaj +1 more source

Joint-Saliency Structure Adaptive Kernel Regression with Adaptive-Scale Kernels for Deformable Registration of Challenging Images

IEEE Access, 2018
This paper proposes a locally adaptive kernel regression with adaptive-scale kernels for deformable image registration with outliers (i.e., missing correspondences and large local deformations).
Binjie Qin +5 more
doaj +1 more source

image registration
unsupervised learning
deep learning

deformable registration
medicine
convolutional neural network

multimodal
machine learning
medical images