Name
Abstract | ||
|---|---|---|
Diffeomorphic registration is widely used in medical image processing with the invertible and one-to-one mapping between images. Recent progress has been made to diffeomorphic registration by utilizing a convolutional neural network for efficient and end-to-end inference of registration fields from an image pair. However, existing deep learning-based registration models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embedding learning, limiting such approaches to identify the semantically meaningful correspondence of anatomical structures. In this paper, we propose a novel dual transformer network (DTN) for diffeomorphic registration, consisting of a learnable volumetric embedding module, a dual cross-image relevance learning module for feature enhancement, and a registration field inference module. The self-attention mechanisms of DTN explicitly model both the inter- and intra-image relevances in the embedding from both the separate and concatenated volumetric images, facilitating semantical correspondence of anatomical structures in diffeomorphic registration. Extensive quantitative and qualitative evaluations demonstrate that the DTN performs favorably against state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87202-1_13 | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT IV |
Keywords | DocType | Volume |
Dual transformer, Diffeomorphic registration, Relevance learning | Conference | 12904 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yungeng Zhang | 1 | 1 | 3.05 |
| Yuru Pei | 2 | 101 | 15.45 |
| Hongbin Zha | 3 | 2206 | 183.36 |