Optics and Precision Engineering, Volume. 30, Issue 10, 1203(2022)
Image registration based on residual mixed attention and multi-resolution constraints
[1] G HASKINS, U KRUGER, P K YAN. Deep learning in medical image registration: a survey. Machine Vision and Applications, 31, 1-18(2020).
[2] [2] 2王宾, 刘林, 侯榆青, 等. 应用改进迭代最近点方法的三维心脏点云配准[J]. 光学 精密工程, 2020, 28(2): 474-484. doi: 10.3788/OPE.20202802.0474WANGB, LIUL, HOUY Q, et al. Three-dimensional cardiac point cloud registration by improved iterative closest point method[J]. Opt. Precision Eng., 2020, 28(2): 474-484.(in Chinese). doi: 10.3788/OPE.20202802.0474
[3] Y GU, X Q LU, B H ZHANG et al. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography. PLoS One, 14(2019).
[4] X Q LU, Y GU, L D YANG et al. Multi-level 3D densenets for false-positive reduction in lung nodule detection based on chest computed tomography. Current Medical Imaging, 16, 1004-1021(2020).
[5] J ASHBURNER. A fast diffeomorphic image registration algorithm. NeuroImage, 38, 95-113(2007).
[6] [6] 6刘坤, 吕晓琪, 谷宇, 等. 快速数字影像重建的2维/3维医学图像配准[J]. 中国图象图形学报, 2016, 21(1): 69-77. doi: 10.11834/jig.20160109LIUK, LYUX Q, GUY, et al. The 2D/3D medical image registration algorithm based on rapid digital image reconstruction[J]. Journal of Image and Graphics, 2016, 21(1): 69-77.(in Chinese). doi: 10.11834/jig.20160109
[7] M KHADER, E SCHIAVI, A B HAMZA. A multicomponent approach to nonrigid registration of diffusion tensor images. Applied Intelligence, 46, 241-253(2017).
[8] Y GU, J Q CHI, J Q LIU et al. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Computers in Biology and Medicine, 137, 104806(2021).
[9] L J QIAN, Q ZHOU, X H CAO et al. A cascade-network framework for integrated registration of liver DCE-MR images. Computerized Medical Imaging and Graphics, 89, 101887(2021).
[10] [10] 10李赛, 黎浩江, 刘立志, 等. 基于尺度注意力沙漏网络的头部MRI解剖点自动定位[J]. 光学 精密工程, 2021, 29(9): 2278-2286. doi: 10.37188/OPE.20212909.2278LIS, LIH J, LIUL Z, et al. Automatic location of anatomical points in head MRI based on the scale attention hourglass network[J]. Opt. Precision Eng., 2021, 29(9): 2278-2286.(in Chinese). doi: 10.37188/OPE.20212909.2278
[11] G R WU, M KIM, Q WANG et al. Scalable high-performance image registration framework by unsupervised deep feature representations learning. IEEE Transactions on Bio-Medical Engineering, 63, 1505-1516(2016).
[12] K A J EPPENHOF, J P W PLUIM. Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks. Journal of Medical Imaging, 5(2018).
[13] M JADERBERG, K SIMONYAN, A ZISSERMAN. Spatial transformer networks. Advances in neural information processing systems, 28, 2017-2025(2015).
[14] E CHEE, Z Z WU. AIRNet: self-supervised affine registration for 3D medical images using neural networks. arXiv preprint arXiv, 2018.
[15] [15] 15谷宇. 基于三维卷积神经网络的低剂量CT肺结节检测[D].上海:上海大学,2019.GUY. Research on Lung Nodule Detection Based on Deep Convolutional Neural Network in Computed Tomography[D]. Shanghai: Shanghai University,2019. (in Chinese)
[16] Y P HU, M MODAT, E GIBSON et al. Weakly-supervised convolutional neural networks for multimodal image registration. Medical Image Analysis, 49, 1-13(2018).
[17] G BALAKRISHNAN, A ZHAO, M R SABUNCU et al. VoxelMorph: a learning framework for deformable medical image registration. IEEE Transactions on Medical Imaging, 38, 1788-1800(2019).
[18] L T ZHANG, L ZHOU, R Y LI et al. Cascaded feature warping network for unsupervised medical image registration, 913-916(2021).
[19] X Y OUYANG, X K LIANG, Y Q XIE. Preliminary feasibility study of imaging registration between supine and prone breast CT in breast cancer radiotherapy using residual recursive cascaded networks. IEEE Access, 9, 3315-3325(2020).
[20] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(2016).
[21] B KIM, J LEE et al. Unsupervised deep learning network with self-attention mechanism for non-rigid registration of 3D brain MR images. Journal of Medical Imaging and Health Informatics, 11, 736-751(2021).
[22] [22] 22秦传波, 宋子玉, 曾军英, 等. 联合多尺度和注意力-残差的深度监督乳腺癌分割[J]. 光学 精密工程, 2021, 29(4): 877-895. doi: 10.37188/OPE.20212904.0877QINC B, SONGZ Y, ZENGJ Y, et al. Deeply supervised breast cancer segmentation combined with multi-scale and attention-residuals[J]. Opt. Precision Eng., 2021, 29(4): 877-895.(in Chinese). doi: 10.37188/OPE.20212904.0877
[23] X L WANG, R GIRSHICK, A GUPTA et al. Non-local neural networks, 7794-7803(2018).
[24] Y J MA, D M NIU, J S ZHANG et al. Unsupervised deformable image registration network for 3D medical images. Applied Intelligence, 52, 766-779(2022).
[25] C R JACK, M A BERNSTEIN, N C FOX et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27, 685-691(2008).
[26] J Y CHEN, Y F HE, E C FREY et al. ViT-V-net: vision transformer for unsupervised volumetric medical image registration. arXiv preprint arXiv:, 2021.
[27] B FISCHL. FreeSurfer. NeuroImage, 62, 774-781(2012).
[28] A DI MARTINO, C G YAN, Q LI et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19, 659-667(2014).
[29] B B AVANTS, C L EPSTEIN, M GROSSMAN et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12, 26-41(2008).
[30] P A YUSHKEVICH, J PIVEN, H C HAZLETT et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage, 31, 1116-1128(2006).
Get Citation
Copy Citation Text
Mingna ZHANG, Xiaoqi LÜ, Yu GU. Image registration based on residual mixed attention and multi-resolution constraints[J]. Optics and Precision Engineering, 2022, 30(10): 1203
Category: Information Sciences
Received: Dec. 22, 2021
Accepted: --
Published Online: Jun. 1, 2022
The Author Email: LÜ Xiaoqi (lxiaoqi@imust.edu.cn)