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 +3 more
doaj +1 more source
Symmetric image registration with directly calculated inverse deformation field [PDF]
, 2012 This paper presents a novel technique for a symmetric deformable image registration based on a new method for fast and accurate direct inversion of a large motion model deformation field.
Matuszewski, Bogdan, Papiez, Bartek
core
Multistep Networks for Deformable Multimodal Medical Image Registration
IEEE AccessWe 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
Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations [PDF]
, 2018 Purpose To evaluate accuracy for 2 deformable image registration methods (in-house B-spline and MIM freeform) using image pairs exhibiting changes in patient orientation and lung volume and to assess the appropriateness of registration accuracy ...
Che, Shaomin +5 more
core +1 more source
Database of Radiotherapy Plan Image for Deformable Image Registration
Japanese Journal of Radiological Technology, 2020 Guidelines require commissioning for deformable image registration (DIR) software before clinical use. The accuracy of DIR software depends upon data used. If common datasets for the DIR commissioning are available, the DIR results using the common datasets would be useful as an accuracy benchmark.
Akihiro, Takemura +5 more
openaire +3 more sources
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
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
Coordinate Translator for Learning Deformable Medical Image Registration
, 2022 The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems ...
Liu, Yihao +5 more
openaire +3 more sources
The Usefulness of Adaptative Radiotherapy in Prostate Cancer: How, When, and Who?
Biomedicines, 2022 The aim of this study was to develop a deformable image registration (DIR)-based offline ART protocol capable of identifying significant dosimetric changes in the first treatment fractions to determine when adaptive replanning is needed.
Rodrigo Muelas-Soria +6 more
doaj +1 more source
Deformable Registration through Learning of Context-Specific Metric Aggregation
, 2017 We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures.
A Cifor +5 more
core +1 more source