Non-Rigid Registration Algorithm of Lung Computed Tomography Image Based on Multi-Scale Parallel Fully Convolutional Neural Network

Lihao Lin; Jianbing Yi; Feng Cao; Wangsheng Fang

doi:10.3788/LOP202259.1617004

[1] Su B, Tao F, Li K et al. Image alignment for synchrotron radiation based X-ray nano-CT[J]. Acta Physica Sinica, 70, 160704(2021).

[2] Tareen S A K, Saleem Z. A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK[C], 17732676(2018).

[3] Liu P N, Xu D D, Bai C M. Scale-invariant feature transform-based heterogeneous image registration method[J]. Laser & Optoelectronics Progress, 58, 2410003(2021).

[4] Yi J B. 4D CT lung motion estimation by robust point matching[D](2017).

[5] Cong M, Wu T, Liu D et al. Prostate MR/TRUS image segmentation and registration methods based on supervised learning[J]. Chinese Journal of Engineering, 42, 1362-1371(2020).

[6] Hu Y P, Modat M, Gibson E et al. Label-driven weakly-supervised learning for multimodal deformable image registration[C], 1070-1074(2018).

[7] Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks[C], 2017-2025(2015).

[8] Balakrishnan G, Zhao A, Sabuncu M R et al. VoxelMorph: a learning framework for deformable medical image registration[J]. IEEE Transactions on Medical Imaging, 38, 1788-1800(2019).

[9] de Vos B D, Berendsen F F, Viergever M A et al. A deep learning framework for unsupervised affine and deformable image registration[J]. Medical Image Analysis, 52, 128-143(2019).

[10] Xue Z Q, Wang Y J. Brain image registration method based on low-resolution auxiliary features and convolutional neural network[J]. Optical Technique, 47, 80-86(2021).

[11] Lu J Y. Lung CT image registration based on convolutional neural network[D](2019).

[12] Fang Q M, Gu X M, Yan J C et al. A FCN-based unsupervised learning model for deformable chest CT image registration[C], 19565241(2019).

[13] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[14] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).

[15] Yu F, Koltun V, Funkhouser T. Dilated residual networks[C], 636-644(2017).

[16] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[17] Zhao M H, Zhong S S, Fu X Y et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 16, 4681-4690(2020).

[18] Zeng A, Wang L J, Pan D et al. Research on medical image registration technology based on FCN and mutual information algorithm[J]. Computer Engineering and Applications, 56, 202-208(2020).

[19] Castillo R, Castillo E, Guerra R et al. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets[J]. Physics in Medicine and Biology, 54, 1849-1870(2009).

[20] Vandemeulebroucke J, Rit S, Kybic J et al. Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs[J]. Medical Physics, 38, 166-178(2011).

[21] Fitzpatrick J M, West J B. The distribution of target registration error in rigid-body point-based registration[J]. IEEE Transactions on Medical Imaging, 20, 917-927(2001).

[22] Eppenhof K A J, Lafarge M W, Moeskops P et al. Deformable image registration using convolutional neural networks[J]. Proceedings of SPIE, 10574, 105740S(2018).

[23] Yi J B, Yang X, Chen G L et al. Lung motion estimation using dynamic point shifting: an innovative model based on a robust point matching algorithm[J]. Medical Physics, 42, 5616-5632(2015).