Cascading Affine and B-spline Registration Method for Large Deformation Registration of Lung X-rays.
Accurate registration of lung X-rays is an important task in medical image analysis. However, the conventional methods usually cost a lot in running time, and the existing deep learning methods are hard to deal with the large deformation caused by respiratory and cardiac motion. In this paper, we at...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 1262 - 1279 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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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=164473092&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473092 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473092 161875986 164473092 164473092 10.1007/s10278-022-00763-z 164473092 ppf: 1262 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Cascading Affine and B-spline Registration Method for Large Deformation Registration of Lung X-rays. aug: au: Chang, Qing Lu, Chenhao Li, Mengke affil: School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China sug: subj: Lung Abnormalities Lung Diseases Radiography Radiography, Thoracic Methods Deep Learning Algorithms Lung Physiology Neural Networks (Computer) Experimental Studies Registration Radiographic Image Interpretation, Computer-Assisted ab: Accurate registration of lung X-rays is an important task in medical image analysis. However, the conventional methods usually cost a lot in running time, and the existing deep learning methods are hard to deal with the large deformation caused by respiratory and cardiac motion. In this paper, we attempt to use deep learning methods to deal with large deformation and enable it to achieve the accuracy of conventional methods. We proposed the cascading affine and B-spline network (CABN), which consists of convolutional cross-stitch affine block (CCAB) and B-splines U-net-like block (BUB) for large lung motion. CCAB makes use of the convolutional cross-stitch model to learn global features among images. And BUB adopts the idea of cubic B-splines which is suitable for large deformation. We separately demonstrated CCAB, BUB, and CABN on two chest X-ray datasets. The experimental results indicate that our methods are highly competitive both in accuracy and runtime when compared to both other deep learning methods and iterative conventional approaches. Moreover, CCAB also can be used for the preprocessing of non-rigid registration methods, replacing affine in conventional methods. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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