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Found 11 papers, 3 papers with code
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology
Between re-stained sections, the median registration error is 2. 3 {\mu}m and 0. 9 {\mu}m in the two subsets of the HyReCo dataset.
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods.
Deformable Groupwise Image Registration using Low-Rank and Sparse Decomposition
Low-rank and sparse decompositions and robust PCA (RPCA) are highly successful techniques in image processing and have recently found use in groupwise image registration.
mlVIRNET: Multilevel Variational Image Registration Network
We show that the use of a deep learning multilevel approach leads to significantly better registration results.
Variational Registration of Multiple Images with the SVD based SqN Distance Measure
Our results indicate that the SqN approach is in fact a suitable distance measure for image registration.
Robust, fast and accurate: a 3-step method for automatic histological image registration
We present a 3-step registration pipeline for differently stained histological serial sections that consists of 1) a robust pre-alignment, 2) a parametric registration computed on coarse resolution images, and 3) an accurate nonlinear registration.
Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking
While deep learning has achieved significant advances in accuracy for medical image segmentation, its benefits for deformable image registration have so far remained limited to reduced computation times.
Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration
Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework.
