https://github.com/Algolzw/SPAC-Deformable-Registration
공식 코드Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
DeepReg: a deep learning toolkit for medical image registration
NCA-Morph: Medical Image Registration with Neural Cellular Automata
Symmetric Transformer-based Network for Unsupervised Image Registration
Enhancing Medical Image Registration via Appearance Adjustment Networks
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
MONAI: An open-source framework for deep learning in healthcare
Quantifying the Resolution of a Template after Image Registration
One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
ModeT: Learning Deformable Image Registration via Motion Decomposition Transformer
Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
NestedMorph: Enhancing Deformable Medical Image Registration with Nested Attention Mechanisms
An Unsupervised Learning Model for Deformable Medical Image Registration
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration
Affine Medical Image Registration with Coarse-to-Fine Vision Transformer
Preserving Tumor Volumes for Unsupervised Medical Image Registration
FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registration
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.