[1] XIAO H N, TENG X Z, LIU C Y et al. A review of deep learning-based three dimensional medical image registration methods[J]. Quantitative Imaging in Medicine and Surgery, 11, 4895-4916(2021).

[2] AVANTS B B, EPSTEIN C L, GROSSMAN M et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain[J]. Medical Image Analysis, 12, 26-41(2008).

[3] FU Y B, LEI Y, WANG T H et al. Deep learning in medical image registration: a review[J]. Physics in Medicine & Biology, 65, 20TR01(2020).

[4] EPPENHOF K A J, PLUIM J P W. Pulmonary CT registration through supervised learning with convolutional neural networks[J]. IEEE Transactions on Medical Imaging, 38, 1097-1105(2019).

[5] HU Y P, MODAT M, GIBSON E et al. Weakly-supervised convolutional neural networks for multimodal image registration[J]. Medical Image Analysis, 49, 1-13(2018).

[6] BALAKRISHNAN G, ZHAO A, SABUNCU M R et al. An unsupervised learning model for deformable medical image registration[C], 9252-9260(2018).

[7] LIN L H, YI J B, CAO F et al. Non-rigid registration algorithm of lung computed tomography image based on multi-scale parallel fully convolutional neural network[J]. Laser & Optoelectronics Progress, 59, 1617004(2022).

[8] WANG P, YAN Y Q, QIAN L J et al. Texture filtering based unsupervised registration methods and its application in liver computed tomography images[J]. Journal of Biomedical Engineering, 38, 819-827(2021).

[9] YANG Y F, WU H H. Deformable medical image registration based on CNN[J]. Journal of X-Ray Science and Technology, 31, 85-94(2023).

[10] SHI T T, GUO Z H, YAN X et al. Water body segmentation in remote sensing images based on multi-scale fusion attention module improved UNet[J]. Chinese Journal of Liquid Crystals and Displays, 38, 397-408(2023).

[11] JADERBERG M, SIMONYAN K, ZISSERMAN A. Spatial transformer networks[C], 2017-2025(2015).

[12] ZHANG M N, LÜ X Q, GU Y. Image registration based on residual mixed attention and multi-resolution constraints[J]. Optics and Precision Engineering, 30, 1203-1216(2022).

[13] GLASNER D, BAGON S, IRANI M. Super-resolution from a single image[C], 349-356(2009).

[14] ZONTAK M, IRANI M. Internal statistics of a single natural image[C], 977-984(2011).

[15] MEI Y Q, FAN Y C, ZHOU Y Q et al. Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining[C], 5689-5698(2020).

[16] LI X T, HE H, LI X et al. PointFlow: flowing semantics through points for aerial image segmentation[C], 4215-4224(2021).

[17] ADELSON E H, ANDERSON C H, BERGEN J R et al. Pyramid methods in image processing[J]. RCA Engineer, 29, 33-41(1984).

[18] FAN J H, BOCUS M J, HOSKING B et al. Multi-scale feature fusion: learning better semantic segmentation for road pothole detection[C], 1-5(2021).

[19] YANG T J, BAI X H, CUI X J et al. DAU-Net: An unsupervised 3D brain MRI registration model with dual-attention mechanism[J]. International Journal of Imaging Systems and Technology, 33, 217-229(2023).

[20] ROY A G, NAVAB N, WACHINGER C. Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks[C], 421-429(2018).

[21] LAMONTAGNE P J, KEEFE S, LAUREN W et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease[J]. Alzheimers & Dementia the Journal of the Alzheimers Association(2018).

[22] SHATTUCK D W, MIRZA M, ADISETIYO V et al. Construction of a 3D probabilistic atlas of human cortical structures[J]. NeuroImage, 39, 1064-1080(2008).

[23] DI MARTINO A, YAN C G, LI Q et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism[J]. Molecular Psychiatry, 19, 659-667(2014).

[24] FISCHL B. FreeSurfer[J]. NeuroImage, 62, 774-781(2012).

[25] KIM B, KIM D H, PARK S H et al. CycleMorph: cycle consistent unsupervised deformable image registration[J]. Medical Image Analysis, 71, 102036(2021).

[26] YANG Y, ZHOU Y, CHEN J N. Hyperspectral image classification based on multi-scale hybrid convolutional network[J]. Chinese Journal of Liquid Crystals and Displays, 38, 368-377(2023).