23 papers · ranked by Valyu relevance
Jing Zou, Bingchen Gao, Youyi Song, Jing Qin
The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of…
Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje, Gulistan Raja
We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPIRE brings distance measures which combine intensity and spatial information into an elastic B-splines-based transformation model and incorporates an inverse inconsistency penalization supporting symmetric registration…
Yiqin Cao, Zhenyu Zhu, Yi Rao, Chenchen Qin + 4 more
'Dong Ni' 'Yi Wang'] Deformable image registration is of essential important for clinical diagnosis, treatment planning, and surgical navigation. However, most existing registration solutions require separate rigid alignment before deformable registration, and may not well handle the large deformation circumstances. We…
Eduard Schreibmann, Paul Pantalone, Anthony Waller, Tim Fox
Deformable registration has migrated from a research topic to a widely used clinical tool that can improve radiotherapeutic treatment accuracy by tracking anatomical changes. Although various mathematical formulations have been reported in the literature and implemented in commercial software, we lack a straightforward…
Michael Figl, Rainer Hoffmann, Marcus Kaar, Johann Hummel + 1 more
'Qinghui Zhang'] US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues…
Ahsan Raza Siyal, Markus Haltmeier, Ruth Steiger, Malik Galijašević + 2 more
Deformable medical image registration is a fundamental task in medical image analysis. While deep learning-based methods have demonstrated superior accuracy and computational efficiency compared to traditional techniques, they often overlook the critical role of regularization in ensuring robustness and anatomical…
Kaicong Sun, Sven Simon
Purpose: Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost…
Andi Li, Yuhan Ying, Tian Gao, Lei Zhang + 4 more
'Guoli Song' 'He Zhang'] Deformable registration plays a fundamental and crucial role in scenarios such as surgical navigation and image-assisted analysis. While deformable registration methods based on unsupervised learning have shown remarkable success in predicting displacement fields with high accuracy, many…
Xiaodan Sui, Yuanjie Zheng, Yunlong He, Weikuan Jia
Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve…
Vasiliki Sideri-Lampretsa, Nil Stolt-Ansó, Martin J. Menten, Huaqi Qiu + 2 more
'Huaqi Qiu' 'Julian McGinnis' 'Daniel Rueckert'] Data-driven deformable image registration methods predominantly rely on operations that process grid-like inputs. However, applying deformable transformations to an image results in a warped space that deviates from a rigid grid structure. Consequently, data-driven…
Zafar Iqbal, Anwar Ul Haq, Srimannarayana Grandhi
Unsupervised deformable image registration requires aligning complex anatomical structures without reference labels, making interpretability and reliability critical. Existing deep learning methods achieve considerable accuracy but often lack transparency, leading to error drift and reduced clinical trust. We propose a…
Max-Heinrich Laves, Sontje Ihler, Tobias Ortmaier
We present deformable unsupervised medical image registration using a randomly-initialized deep convolutional neural network (CNN) as regularization prior. Conventional registration methods predict a transformation by minimizing dissimilarities between an image pair. The minimization is usually regularized with…
Carl Sjöberg, Silvia Johansson, Anders Ahnesjö
Background and purpose Multi-atlas segmentation can yield better results than single atlas segmentation, but practical applications are limited by long calculation times for deformable registration. To shorten the calculation time pre-calculated registrations of atlases could be linked via a single atlas registered in…
Joel Honkamaa, Pekka Marttinen
Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordinate systems of two images. Popular classical image registration methods enforce the useful inductive biases of symmetricity, inverse…
Bramsh Qamar Chandio, Emanuele Olivetti, David Romero-Bascones, Jaroslaw Harezlak + 1 more
Nonlinear registration plays a central role in most neuroimage analysis methods and pipelines, such as in tractography-based individual and group-level analysis methods. However, nonlinear registration is a non-trivial task, especially when dealing with tractography data that digitally represent the underlying anatomy…
Soumyadeep Pal, Matthew Tennant, Nilanjan Ray
—We present a postprocessing layer for deformable image registration to make a registration field more diffeomorphic by encouraging Jacobians of the transformation to be positive. Diffeomorphic image registration is important for medical imaging studies because of the properties like invertibility, smoothness of the…
Fan Zhang, William M. Wells, Lauren J. O’Donnell
In this paper, we present a deep learning method, DDMReg, for fast and accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract…
Istvan N. Huszar, Menuka Pallebage-Gamarallage, Sarah Bangerter-Christensen, Hannah Brooks + 14 more
Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited…
M. Rahmani, H. Moghadassi, P. Farnia, A. Ahmadian
In neurosurgery, image guidance is provided based on the patient to pre-operative data registration with a neuronavigation system. However, the brain shift phenomena invalidate the accuracy of the navigation system during neurosurgery. One of the most common approaches for brain shift compensation is using…
J. Eugenio Iglesias, Ian P. Johnson, Jonathan Williams-Ramirez, Dina Zemlyanker + 10 more
Portable low-field MRI offers an affordable and mobile alternative to conventional high-field scanners, enabling imaging in point-of-care and resource-limited settings. However, its lower signal-to-noise ratio, reduced resolution, and acquisition artifacts raise concerns about the accuracy of standard image…
Junyu Chen, Yihao Liu, Shuwen Wei, Zhangxing Bian + 4 more
'Shalini Subramanian' 'Aaron Carass' 'Jerry L. Prince' 'Yong Du'] Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image…
Iman Aganj, Bruce Fischl
The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible…
Gregory A. Landrum, Jessica Braun, Paul Katzberger, Marc T. Lehner + 1 more
Here, we present lwreg, a lightweight, yet flexible chemical registration system supporting the capture of both two-dimensional molecular structures (topologies) and three-dimensional conformers. lwreg is open source, with a simple Python API and is designed to be easily integrated into computational workflows. In…