4 citations · 4 across the 14 of their papers we have counts for
9 papers · 1 filter
MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis
Jinwei Zhang, Lianrui Zuo, Yihao Liu +10
In multiple sclerosis, lesions interfere with automated magnetic resonance imaging analyses such as brain parcellation and deformable registration, while lesion segmentation models…
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…
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
Lasse Hansen, Wiebke Heyer, Christoph Großbröhmer +51
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the…
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…
Beyond the LUMIR challenge: The pathway to foundational registration models
Junyu Chen, Shuwen Wei, Joel Honkamaa +33
Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundati…