4 citations · 6 across the 15 of their papers we have counts for
15 papers
Unsupervised MR-US Multimodal Image Registration with Multilevel Correlation Pyramidal Optimization
Jiazheng Wang, Zeyu Liu, Min Liu +4
Surgical navigation based on multimodal image registration has played a significant role in providing intraoperative guidance to surgeons by showing the relative position of the ta…
FMIR, a foundation model-based Image Registration Framework for Robust Image Registration
Fengting Zhang, Yue He, Qinghao Liu +3
Deep learning has revolutionized medical image registration by achieving unprecedented speeds, yet its clinical application is hindered by a limited ability to generalize beyond th…
Ideal Registration? Segmentation is All You Need
Xiang Chen, Fengting Zhang, Qinghao Liu +4
Deep learning has revolutionized image registration by its ability to handle diverse tasks while achieving significant speed advantages over conventional approaches. Current approa…
SAMIR, an efficient registration framework via robust feature learning from SAM
Yue He, Min Liu, Qinghao Liu +4
Image registration is a fundamental task in medical image analysis. Deformations are often closely related to the morphological characteristics of tissues, making accurate feature…
Encoder-Only Image Registration
Xiang Chen, Renjiu Hu, Jinwei Zhang +5
Learning-based techniques have significantly improved the accuracy and speed of deformable image registration. However, challenges such as reducing computational complexity and han…
Gaussian Primitive Optimized Deformable Retinal Image Registration
Xin Tian, Jiazheng Wang, Yuxi Zhang +5
Deformable retinal image registration is notoriously difficult due to large homogeneous regions and sparse but critical vascular features, which cause limited gradient signals in s…
VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registration
Hang Zhang, Yuxi Zhang, Jiazheng Wang +5
Recent developments in neural networks have improved deformable image registration (DIR) by amortizing iterative optimization, enabling fast and accurate DIR results. However, lear…
Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing
Hang Zhang, Xiang Chen, Renjiu Hu +7
Learning-based deformable image registration (DIR) accelerates alignment by amortizing traditional optimization via neural networks. Label supervision further enhances accuracy, en…
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
Jiacheng Wang, Xiang Chen, Renjiu Hu +5
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of hum…
Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024
Yuxi Zhang, Xiang Chen, Jiazheng Wang +5
In this paper, we summarize the methods and experimental results we proposed for Task 2 in the learn2reg 2024 Challenge. This task focuses on unsupervised registration of anatomica…
Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization
Jiazheng Wang, Xiang Chen, Yuxi Zhang +3
Surgical navigation based on multimodal image registration has played a significant role in providing intraoperative guidance to surgeons by showing the relative position of the ta…
MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filters
Hang Zhang, Xiang Chen, Renjiu Hu +3
Many existing learning-based deformable image registration methods impose constraints on deformation fields to ensure they are globally smooth and continuous. However, this assumpt…
Slicer Networks
Hang Zhang, Xiang Chen, Rongguang Wang +3
In medical imaging, scans often reveal objects with varied contrasts but consistent internal intensities or textures. This characteristic enables the use of low-frequency approxima…
Spatially Covariant Image Registration with Text Prompts
Xiang Chen, Min Liu, Rongguang Wang +4
Medical images are often characterized by their structured anatomical representations and spatially inhomogeneous contrasts. Leveraging anatomical priors in neural networks can gre…
Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning
Ting Zhou, Kang Zhou, Jianlong Yang +7
Optical coherence tomography angiography (OCTA) requires high transverse sampling density for visualizing retinal and choroidal capillaries. Low transverse sampling causes resoluti…