Medical image registration International Challenge, Learn2Reg 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 27-October 4, 2025, Proceedings Junyu Chen, Aaron Carass, Mattias Heinrich, Reuben Dorent, editors

Format:

Book

Conference/Event

Contributor:

Chen, Junyu, editor.

Carass, Aaron, editor.

Heinrich, Mattias P., editor.

Dorent, Reuben, editor.

Conference Name:

Learn2Reg (Challenge) (2025 : Taejŏn-si, Korea)

International Conference on Medical Image Computing and Computer-Assisted Intervention (28th : 2025 : Taejŏn-si, Korea)

Series:

Lecture notes in computer science ; 1611-3349 16254

Lecture notes in computer science 1611-3349 16254

Language:

English

Subjects (All):

Diagnostic imaging--Data processing--Congresses.

Diagnostic imaging.

Diagnostic imaging--Digital techniques--Congresses.

Image registration--Congresses.

Image registration.

Physical Description:

1 online resource

illustration

Other Title:

Learn2Reg 2025

Place of Publication:

Cham Springer 2026

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the International Challenge on Medical Image Registration, Learn2Reg 2025, held in conjunction with MICCAI 2025, which took place in Daejeon, South Korea, during September 27-October 4, 2025. The 11 full papers presented in the proceedings were carefully selected and reviewed from 38 submissions. The papers cover two principle sub-tasks: ReMIND2Reg and LUMIR

Contents:

ReMIND2Reg.

Unsupervised MR-US Multimodal Image Registration with Multilevel Correlation Pyramidal Optimization.

In Gradients We Trust: NGF-Driven Registration for ReMIND 2025.

Gabor-Based Neighborhood Descriptor for MRI-iUS Brain Image Registration.

LUMIR.

Zero-shot Multi-Contrast Brain MRI Registration by Intensity Randomizing T1-weighted MRI (LUMIR25).

Strategies for Robust Deep Learning Based De formable Registration.

Unleashing the power of intensity augmentation for multi-modal image registration.

Adapting Frozen Mono-modal Backbones for Multi modal Registration via Contrast-Agnostic Instance Optimization.

Generalizable Learning-based Image Registration via Self-Supervised Multi-modal Representation Learning from Single-modal Data.

Swin-CNN Hybrid Framework for Enhanced De formable Image Registration.

Efficient Unsupervised Multimodal Brain MR Image Registration with Encoder-only Network

Notes:

Online resource; title from PDF title page (SpringerLink, viewed July 15, 2026)

ISBN:

9783032251695

3032251699

OCLC:

1604042938