Image synthesis-based multi-modal image registration framework by using deep fully convolutional networks.
Multi-modal image registration has significant meanings in clinical diagnosis, treatment planning, and image-guided surgery. Since different modalities exhibit different characteristics, finding a fast and accurate correspondence between images of different modalities is still a challenge. In this p...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 5; pp. 1037 - 1049 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2019
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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=135997064&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135997064 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2019 vid: 57 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135997064 135997064 NLM30523534 10.1007/s11517-018-1924-y NLM30523534 135997064 ppf: 1037 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Image synthesis-based multi-modal image registration framework by using deep fully convolutional networks. aug: au: Liu, Xueli Jiang, Dongsheng Wang, Manning Song, Zhijian affil: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China sug: subj: Brain Diagnostic Imaging Methods Image Processing, Computer Assisted Methods Resource Databases Phantoms, Imaging Neural Networks (Computer) Magnetic Resonance Imaging Methods Clinical Assessment Tools Scales ab: Multi-modal image registration has significant meanings in clinical diagnosis, treatment planning, and image-guided surgery. Since different modalities exhibit different characteristics, finding a fast and accurate correspondence between images of different modalities is still a challenge. In this paper, we propose an image synthesis-based multi-modal registration framework. Image synthesis is performed by a ten-layer fully convolutional network (FCN). The network is composed of 10 convolutional layers combined with batch normalization (BN) and rectified linear unit (ReLU), which can be trained to learn an end-to-end mapping from one modality to the other. After the cross-modality image synthesis, multi-modal registration can be transformed into mono-modal registration. The mono-modal registration can be solved by methods with lower computational complexity, such as sum of squared differences (SSD). We tested our method in T1-weighted vs T2-weighted, T1-weighted vs PD, and T2-weighted vs PD image registrations with BrainWeb phantom data and IXI real patients' data. The result shows that our framework can achieve higher registration accuracy than the state-of-the-art multi-modal image registration methods, such as local mutual information (LMI) and α-mutual information (α-MI). The average registration errors of our method in experiment with IXI real patients' data were 1.19, 2.23, and 1.57 compared to 1.53, 2.60, and 2.36 of LMI and 1.34, 2.39, and 1.76 of α-MI in T2-weighted vs PD, T1-weighted vs PD, and T1-weighted vs T2-weighted image registration, respectively. In this paper, we propose an image synthesis-based multi-modal image registration framework. A deep FCN model is developed to perform image synthesis for this framework, which can capture the complex nonlinear relationship between different modalities and discover complex structural representations automatically by a large number of trainable mapping and parameters and perform accurate image synthesis. The framework combined with the deep FCN model and mono-modal registration methods (SSD) can achieve fast and robust results in multi-modal medical image registration. Graphical abstract The workflow of proposed multi-modal image registration framework. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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