Medical 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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hsokooti/regun
공식 코드Quantitative Error Prediction of Medical Image Registration using Regression Forests
https://github.com/iamlab-ryerson/flowreg
공식 코드FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow
https://github.com/juliuswang-7/pan
공식 코드https://github.com/junyuchen245/Fully_Unsupervised_CNN_Registration_Keras
공식 코드https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration
공식 코드TransMorph: Transformer for unsupervised medical image registration
https://github.com/junyuchen245/ViT-V-Net_for_3D_Image_Registration_Pytorch
공식 코드ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
https://github.com/junyuchen245/pretraining_image_registration_dnns
공식 코드Pretraining Deformable Image Registration Networks with Random Images
https://github.com/jw4hv/deepflash
공식 코드DeepFLASH: An Efficient Network for Learning-based Medical Image Registration
https://github.com/kangmiao15/dual-stream-prnet-plus
공식 코드https://github.com/louelin/quiz
공식 코드QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration
https://github.com/louisvh/cycle_consistent_inr
공식 코드Robust deformable image registration using cycle-consistent implicit representations
https://github.com/m1nhengchen/sopi
공식 코드https://github.com/mazurowski-lab/foundation-based-reg
공식 코드Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
https://github.com/medvic-lab/efficient_morph_registration
공식 코드EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration
https://github.com/microsoft/Recursive-Cascaded-Networks
공식 코드Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network
https://github.com/miraclefactory/on-the-fly-guidance
공식 코드https://github.com/multimodallearning/convexadam
공식 코드Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021
https://github.com/multimodallearning/registration-da-mean-teacher
공식 코드Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts
https://github.com/mungomeng/registration-aan
공식 코드Enhancing Medical Image Registration via Appearance Adjustment Networks
https://github.com/mungomeng/registration-autofuse
공식 코드AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
https://github.com/mungomeng/registration-corrmlp
공식 코드Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration
https://github.com/neel-dey/Atlas-GAN
공식 코드Generative Adversarial Registration for Improved Conditional Deformable Templates
https://github.com/olegl/distort
공식 코드Registration of serial sections: An evaluation method based on distortions of the ground truths
https://github.com/pragyanstha/rayemb
공식 코드RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace
https://github.com/promise13/fm_sam
공식 코드Improving Generalization of Medical Image Registration Foundation Model
https://github.com/qiangwang57/coreg_flim_histology
공식 코드https://github.com/rohitrango/Magic-or-Mirage
공식 코드Deep Learning in Medical Image Registration: Magic or Mirage?
https://github.com/ruizhe-l/mrregnet
공식 코드https://github.com/runshi-zhang/utsrmorph
공식 코드https://github.com/solemoon/xmorpher
공식 코드XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
https://github.com/soumickmj/micdir
공식 코드https://github.com/sqhuang0103/samreg
공식 코드SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
https://github.com/uncbiag/ByConstructionICON
공식 코드Inverse Consistency by Construction for Multistep Deep Registration
https://github.com/uncbiag/ICON
공식 코드$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
https://github.com/uncbiag/equivariant_reg_2
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.