Medical Image Registration
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 4
- 보존된 평가 기록
- 18
Image registration, also known as image fusion or image matching, is the process of aligning two or more images based on image appearances. Medical Image Registration seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures. Medical Image Registration is used in many clinical applications such as image guidance, motion tracking, segmentation, dose accumulation, image reconstruction and so on. Medical Image Registration is a broad topic which can be grouped from various perspectives. From input image point of view, registration methods can be divided into unimodal, multimodal, interpatient, intra-patient (e.g. same- or different-day) registration. From deformation model point of view, registration methods can be divided in to rigid, affine and deformable methods. From region of interest (ROI) perspective, registration methods can be grouped according to anatomical sites such as brain, lung registration and so on. From image pair dimension perspective, registration methods can be divided into 3D to 3D, 3D to 2D and 2D to 2D/3D.
Source: Deep Learning in Medical Image Registration: A Review
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
Implicit Deformable Medical Image Registration with Learnable Kernels
Pretraining Deformable Image Registration Networks with Random Images
Improving Generalization of Medical Image Registration Foundation Model
FF-PNet: A Pyramid Network Based on Feature and Field for Brain Image Registration
Tetrahedron-Net for Medical Image Registration
IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration
OncoReg: Medical Image Registration for Oncological Challenges
SACB-Net: Spatial-awareness Convolutions for Medical Image Registration
NCF: Neural Correspondence Field for Medical Image Registration
Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging
Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention
From Model Based to Learned Regularization in Medical Image Registration: A Comprehensive Review
LDM-Morph: Latent diffusion model guided deformable image registration
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment
A Symmetric Dynamic Learning Framework for Diffeomorphic Medical Image Registration
NCA-Morph: Medical Image Registration with Neural Cellular Automata
UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration
GESH-Net: Graph-Enhanced Spherical Harmonic Convolutional Networks for Cortical Surface Registration
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace
NestedMorph: Enhancing Deformable Medical Image Registration with Nested Attention Mechanisms
Dual-Attention Frequency Fusion at Multi-Scale for Joint Segmentation and Deformable Medical Image Registration
Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization
H-SGANet: Hybrid Sparse Graph Attention Network for Deformable Medical Image Registration
Reliable Multi-modal Medical Image-to-image Translation Independent of Pixel-wise Aligned Data
Deep Learning in Medical Image Registration: Magic or Mirage?
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
General Vision Encoder Features as Guidance in Medical Image Registration
WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration
Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration
Biomechanics-informed Non-rigid Medical Image Registration and its Inverse Material Property Estimation with Linear and Nonlinear Elasticity
Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual Learning
Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.