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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/uncbiag/registration
공식 코드https://github.com/uncbiag/unigradicon
공식 코드uniGradICON: A Foundation Model for Medical Image Registration
https://github.com/vboussot/impactloss
공식 코드IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration
https://github.com/vignywang/awesome-local-feature-matching
공식 코드https://github.com/vigsivan/optimization-based-registration
공식 코드Recurrence With Correlation Network for Medical Image Registration
https://github.com/voxelmorph/voxelmorph
공식 코드VoxelMorph: A Learning Framework for Deformable Medical Image Registration
https://github.com/wangshansong1/Triad
공식 코드Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging
https://github.com/wjiazheng/mcbo
공식 코드https://github.com/wujiong-hub/ldm-morph
공식 코드LDM-Morph: Latent diffusion model guided deformable image registration
https://github.com/x-xc/WiNet
공식 코드WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration
https://github.com/x-xc/sacb_net
공식 코드SACB-Net: Spatial-awareness Convolutions for Medical Image Registration
https://github.com/xi-jia/fourier-net
공식 코드Fourier-Net: Fast Image Registration with Band-limited Deformation
https://github.com/xi-jia/lessnet
공식 코드https://github.com/xi-jia/lku-net
공식 코드U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
https://github.com/xiongchaochen/dusfe_crossregistration
공식 코드Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT
https://github.com/zax130/modetv2
공식 코드https://github.com/zhemin-1992/registration_pinns
공식 코드Non-rigid Medical Image Registration using Physics-informed Neural Networks
https://gitlab.com/franco.stramana1/slice-to-volume
공식 코드Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields
https://github.com/ANTsX/ANTs
https://github.com/Borda/BIRL
BIRL: Benchmark on Image Registration methods with Landmark validation
https://github.com/BoyOfChu/LL_Net
https://github.com/CIG-UCL/eddeep
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
https://github.com/CIG-UCL/polaffini
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
https://github.com/ChristophJud/airlab
https://github.com/GeeLaw/bio-site
Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network
https://github.com/HastingsGreer/InverseConsistency
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
https://github.com/MindSpore-scientific/code-11/tree/main/Quiz_gen
QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration
https://github.com/MindSpore-scientific/code-12/tree/main/Fourier-Features-Let-Networks-Learn-High-Frequency
Networks for Joint Affine and Non-parametric Image Registration
https://github.com/ZAX130/CorrXCA-Unofficial
Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention
https://github.com/acasamitjana/3dhirest
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
https://github.com/airlab-unibas/MICCAITutorial2019
https://github.com/biomedia-mira/drop2
https://github.com/fiji/bUnwarpJ
https://github.com/fjr9516/dbm_with_dl
Generative Adversarial Registration for Improved Conditional Deformable Templates
https://github.com/ivan-jgr/recursive-cascaded-networks
Recursive Cascaded Networks for Unsupervised Medical Image Registration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.