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Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning

Abstract

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org . Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods
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Category

Academic article

Language

English

Author(s)

  • Alessa Hering
  • Lasse Hansen
  • Tony C. W. Mok
  • Albert C. S. Chung
  • Hanna Siebert
  • Stephanie Häger
  • Annkristin Lange
  • Sven Kuckertz
  • Stefan Heldmann
  • Wei Shao
  • Sulaiman Vesal
  • Mirabela Rusu
  • Geoffrey Sonn
  • Théo Estienne
  • Maria Vakalopoulou
  • Luyi Han
  • Yunzhi Huang
  • Pew-Thian Yap
  • Mikael Brudfors
  • Yaël Balbastre
  • Samuel Joutard
  • Marc Modat
  • Gal Lifshitz
  • Dan Raviv
  • Jinxin Lv
  • Quang Li
  • Vincent Jaouen
  • Dimitris Visvikis
  • Constance Fourcade
  • Mathieu Rubeaux
  • Wentao Pan
  • Zhe Xu
  • Bailiang Jian
  • Francesca De Benetti
  • Marek Wodzinski
  • Niklas Gunnarsson
  • Jens Sjölund
  • Daniel Grzech
  • Huaqi Qiu
  • Zeju Li
  • Alexander Thorley
  • Jinming Duan
  • Christoph Grossbröhmer
  • Andrew Hoopes
  • Ingerid Reinertsen
  • Yiming Xiao
  • Bennett Landman
  • Yuankai Huo
  • Keelin Murphy
  • Nikolas Lessmann
  • Bram van Ginneken
  • Adrian V. Dalca
  • Mattias P. Heinrich

Affiliation

  • SINTEF Digital / Health Research
  • Uppsala University
  • France
  • University of Paris-Saclay
  • Radboud University
  • United Kingdom
  • Imperial College London
  • King's College London
  • University of Birmingham
  • Geneva School of Business Administration - University of Applied Sciences Western Switzerland
  • Germany
  • University Lübeck
  • Hong Kong University of Science and Technology
  • The Chinese University of Hong Kong
  • Tel Aviv University
  • Huazhong University of Science and Technology
  • Tsinghua University
  • Nanjing University of Information Science & Technology (NUIST)
  • Concordia University
  • Stanford University
  • University of North Carolina at Chapel Hill
  • Vanderbilt University
  • Massachusetts General Hospital
  • Harvard Medical School

Year

2022

Published in

IEEE Transactions on Medical Imaging

ISSN

0278-0062

Volume

42

Issue

3

Page(s)

697 - 712

View this publication at Norwegian Research Information Repository