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Medical Image Registration

33 평가 기록 8 datasets

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.

<span class="description-source">Source: Deep Learning in Medical Image Registration: A Review </span>

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
IXI
LL_Net
DSC: 0.767
논문 코드
TransMorph
DSC: 0.818val dsc: 85.8
논문 코드
NiftyReg (CC)
Dice (Average): 0.755CPU (sec): 2347Neg Jacob Det: 33838Dice (SE): 0.143
- -
cVAE Diffeomorphic (S3)
Dice: 0.812RMSE: 0.30Hausdorff Distance (mm): 7.3Grad Det-Jac: 1.4
논문 -
ANTs
AMrTRE: 2.3MMrTRE: 1.67
논문 코드
Metric Net (Local Reg)
Mean target overlap ratio: 0.520
논문 코드
MambaMorph
Dice (Average): 82.71
논문 코드

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