Residual Aligner Network
Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartlomiej W., Papiez

TL;DR
This paper introduces a novel Motion-Aware structure with a Residual Aligner module for improved 3D medical image registration, effectively capturing regional motions and outperforming existing methods in accuracy and efficiency.
Contribution
The paper proposes a new MA structure and RA module that better model regional motions, leading to more accurate and efficient 3D image registration in medical imaging.
Findings
Achieved top accuracy in unsupervised abdominal CT registration.
Matched state-of-the-art results in lung segmentation.
Validated the theoretical analysis of motion patterns.
Abstract
Image registration is important for medical imaging, the estimation of the spatial transformation between different images. Many previous studies have used learning-based methods for coarse-to-fine registration to efficiently perform 3D image registration. The coarse-to-fine approach, however, is limited when dealing with the different motions of nearby objects. Here we propose a novel Motion-Aware (MA) structure that captures the different motions in a region. The MA structure incorporates a novel Residual Aligner (RA) module which predicts the multi-head displacement field used to disentangle the different motions of multiple neighbouring objects. Compared with other deep learning methods, the network based on the MA structure and RA module achieve one of the most accurate unsupervised inter-subject registration on the 9 organs of assorted sizes in abdominal CT scans, with the…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Medical Imaging Techniques and Applications · Radiomics and Machine Learning in Medical Imaging
