Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration

Bailiang Jian, Jiazhen Pan, Morteza Ghahremani, Daniel Rueckert, Christian Wachinger, Benedikt Wiestler

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

VoxelMorph, proposed in 2018, utilizes Convolutional Neural Networks (CNNs) to address medical image registration problems. In 2021 TransMorph advanced this approach by replacing CNNs with Attention mechanisms, claiming enhanced performance. More recently, the rise of Mamba with selective state space models has led to MambaMorph, which substituted Attention with Mamba blocks, asserting superior registration. These developments prompt a critical question: does chasing the latest computational trends with “more advanced” computational blocks genuinely enhance registration accuracy, or is it merely hype? Furthermore, the role of classic high-level registration-specific designs, such as coarse-to-fine pyramid mechanism, correlation calculation, and iterative optimization, warrants scrutiny, particularly in differentiating their influence from the aforementioned low-level computational blocks. In this study, we critically examine these questions through a rigorous evaluation in brain MRI registration. We employed modularized components for each block and ensured unbiased comparisons across all methods and designs to disentangle their effects on performance. Our findings indicate that adopting “advanced” computational elements fails to significantly improve registration accuracy. Instead, well-established registration-specific designs offer fair improvements, enhancing results by a marginal 1.5% over the baseline. Our findings emphasize the importance of rigorous, unbiased evaluation and contribution disentanglement of all low- and high-level registration components, rather than simply following the computer vision trends with “more advanced” computational blocks. We advocate for simpler yet effective solutions and novel evaluation metrics that go beyond conventional registration accuracy, warranting further research across various organs and modalities.

OriginalspracheEnglisch
TitelBiomedical Image Registration - 11th International Workshop, WBIR 2024, Held in Conjunction with MICCAI 2024, Proceedings
Redakteure/-innenMarc Modat, Žiga Špiclin, Alessa Hering, Ivor Simpson, Wietske Bastiaansen, Tony C. W. Mok
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten86-97
Seitenumfang12
ISBN (Print)9783031734793
DOIs
PublikationsstatusVeröffentlicht - 2024
Veranstaltung11th International Workshop on Biomedical Image Registration, WBIR 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024 - Marrakesh, Marokko
Dauer: 6 Okt. 20246 Okt. 2024

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band15249 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz11th International Workshop on Biomedical Image Registration, WBIR 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
Land/GebietMarokko
OrtMarrakesh
Zeitraum6/10/246/10/24

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