DELR-Net: a network for 3D multimodal medical image registration in more lightweight application scenarios.
Purpose: 3D multimodal medical image deformable registration plays a significant role in medical image analysis and diagnosis. However, due to the substantial differences between images of different modalities, registration is challenging and requires high computational costs. Deep learning-based re...
| Publicado en: | Abdominal Radiology Vol. 50; no. 4; pp. 1876 - 1887 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184038966&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184038966 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Apr2025 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184038966 180247290 10.1007/s00261-024-04602-3 184038966 ppf: 1876 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: DELR-Net: a network for 3D multimodal medical image registration in more lightweight application scenarios. aug: au: Deng, Liwei Lan, Qi Yang, Xin Wang, Jing Huang, Sijuan affil: https://ror.org/04e6y1282 Harbin University of Science and Technology, Harbin, China sug: ab: Purpose: 3D multimodal medical image deformable registration plays a significant role in medical image analysis and diagnosis. However, due to the substantial differences between images of different modalities, registration is challenging and requires high computational costs. Deep learning-based registration methods face these challenges. The primary aim of this paper is to design a 3D multimodal registration network that ensures high-quality registration results while reducing the number of parameters. Methods: This study designed a Dual-Encoder More Lightweight Registration Network (DELR-Net). DELR-Net is a low-complexity network that integrates Mamba and ConvNet. The State Space Sequence Module and the Dynamic Large Kernel block are used as the main components of the dual encoders, while the Dynamic Feature Fusion block is used as the main component of the decoder. Results: This study conducted experiments on 3D brain MR images and abdominal MR and CT images. Compared to existing registration methods, DELR-Net achieved better registration results while maintaining a lower number of parameters. Additionally, generalization experiments on other modalities showed that DELR-Net has superior generalization capabilities. Conclusion: DELR-Net significantly improves the limitations of 3D multimodal medical image deformable registration, achieving better registration performance with fewer parameters. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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