InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration

Template generation is a critical step in groupwise image registration, which involves aligning a group of subjects into a common space. While existing methods can generate high-quality template images, they often incur substantial time costs or are limited by fixed group scales. In this paper, we p...

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Vydané v:IEEE transactions on image processing Ročník 34; s. 4778 - 4791
Hlavní autori: He, Ziyi, Chung, Albert C. S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Algorithms
Brain
Brain - diagnostic imaging
brain MRI images
Databases, Factual
Decoding
Deep generative models
Deformation
groupwise registration
Humans
Image Processing, Computer-Assisted - methods
Image quality
Image registration
Imaging, Three-Dimensional - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Modules
Optimization
Registration
Runtime
template generation
Three-dimensional displays
Training
Vectors
ISSN:1057-7149, 1941-0042, 1941-0042
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Popis
Shrnutí:Template generation is a critical step in groupwise image registration, which involves aligning a group of subjects into a common space. While existing methods can generate high-quality template images, they often incur substantial time costs or are limited by fixed group scales. In this paper, we present InstantGroup, an efficient groupwise template generation framework based on variational autoencoder (VAE) models that leverage latent representations' arithmetic properties, enabling scalability to groups of any size. InstantGroup features a Dual VAE backbone with shared-weight twin networks to handle pairs of inputs and incorporates a Displacement Inversion Module (DIM) to maintain template unbiasedness and a Subject-Template Alignment Module (STAM) to improve template quality and registration accuracy. Experiments on 3D brain MRI scans from the OASIS and ADNI datasets reveal that InstantGroup dramatically reduces runtime, generating templates within seconds for various group sizes while maintaining superior performance compared to state-of-the-art baselines on quantitative metrics, including unbiasedness and registration accuracy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2025.3587597