• Laser & Optoelectronics Progress
  • Vol. 59, Issue 14, 1415024 (2022)
Yu Zhang1,2, Haoran Li1,2, Cheng Li1, Fei Li1, and Shanshan Wang1,*
Author Affiliations
  • 1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, Guangdong , China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
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    DOI: 10.3788/LOP202259.1415024 Cite this Article Set citation alerts
    Yu Zhang, Haoran Li, Cheng Li, Fei Li, Shanshan Wang. Combinatorial Reconstruction and Segmentation of Magnetic Resonance Image Using Teacher Forcing[J]. Laser & Optoelectronics Progress, 2022, 59(14): 1415024 Copy Citation Text show less
    References

    [1] Bruno F, Arrigoni F, Palumbo P et al. New advances in MRI diagnosis of degenerative osteoarthropathy of the peripheral joints[J]. La Radiologia Medica, 124, 1121-1127(2019).

    [2] Ebrahimzadeh E, Fayaz F, Ahmadi F et al. A machine learning-based method in order to diagnose lumbar disc herniation disease by MR image processing[EB/OL]. http://www.medtextpublications.com/medlife/articles/MOA-0002.pdf

    [3] Cao P, Shan X F, Zhao D Z et al. Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer’s disease[J]. Pattern Recognition, 72, 219-235(2017).

    [4] Taylor S A, Mallett S, Ball S et al. Diagnostic accuracy of whole-body MRI versus standard imaging pathways for metastatic disease in newly diagnosed non-small-cell lung cancer: the prospective Streamline L trial[J]. The Lancet Respiratory Medicine, 7, 523-532(2019).

    [5] Beddy P, O’Neill A C, Yamamoto A K et al. FIGO staging system for endometrial cancer: added benefits of MR imaging[J]. Radiographics, 32, 241-254(2012).

    [6] Debette S, Schilling S, Duperron M G et al. Clinical significance of magnetic resonance imaging markers of vascular brain injury: a systematic review and meta-analysis[J]. JAMA Neurology, 76, 81-94(2019).

    [7] Wang X Q, Yang F, Cao B et al. Application of convolution neural network in diagnosis of thyroid nodules[J]. Laser & Optoelectronics Progress, 59, 0810005(2022).

    [8] Aggarwal H K, Mani M P, Jacob M. MoDL: model-based deep learning architecture for inverse problems[J]. IEEE Transactions on Medical Imaging, 38, 394-405(2019).

    [9] Kabkab M, Samangouei P, Chellappa R. Task-aware compressed sensing with generative adversarial networks[EB/OL]. https://arxiv.org/abs/1802.01284

    [10] Wang S, Li C, Wang R et al. Annotation-efficient deep learning for automatic medical image segmentation[J]. Nature Communications, 12, 5915(2021).

    [11] Zhang H, Qiu D W, Feng Y B et al. Improved U-Net models and its applications in medical image segmentation: a review[J]. Laser & Optoelectronics Progress, 59, 0200005(2022).

    [12] Wang S S, Su Z H, Ying L et al. Accelerating magnetic resonance imaging via deep learning[J]. IEEE International Symposium on Biomedical Imaging, 2016, 514-517(2016).

    [13] Zhu B, Liu J Z, Cauley S F et al. Image reconstruction by domain-transform manifold learning[J]. Nature, 555, 487-492(2018).

    [14] Zhang D Q, Pang Y W, Liu X H. Reconstruction of dual-domain crossed magnetic resonance images based on codec network[J]. Laser & Optoelectronics Progress, 59, 1210014(2022).

    [15] Duan J Z, He X X, Liu C et al. Method of magnetic resonance imaging reconstruction based on Lp-norm joint total variation[J]. Laser & Optoelectronics Progress, 58, 2411001(2021).

    [16] Mou H W, Guo Y, Quan X H et al. Magnetic resonance imaging brain tumor image segmentation based on improved U-Net[J]. Laser & Optoelectronics Progress, 58, 041022(2021).

    [17] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

    [18] Zhang H L, Li Q, Guan X. An improved three-dimensional dual-path brain tumor image segmentation network[J]. Acta Optica Sinica, 41, 0310002(2021).

    [19] Schlemper J, Oktay O, Bai W J et al. Cardiac MR segmentation from undersampled k-space using deep latent representation learning[M]. Frangi A F, Schnabel J A, Davatzikos C, et al. Medical image computing and computer assisted intervention-MICCAI 2018, 11070, 259-267(2018).

    [20] Huang Q Y, Chen X, Metaxas D et al. Brain segmentation from k-space with end-to-end recurrent attention network[M]. Shen D G, Liu T M, Peters T M, et al. Medical image computing and computer assisted intervention-MICCAI 2019, 11766, 275-283(2019).

    [21] Sun L Y, Fan Z W, Ding X H et al. Joint CS-MRI reconstruction and segmentation with a unified deep network[M]. Chung A C S, Gee J C, Yushkevich P A, et al. Information processing in medical imaging, 11492, 492-504(2019).

    [22] Williams R J, Zipser D. A learning algorithm for continually running fully recurrent neural networks[J]. Neural Computation, 1, 270-280(1989).

    [23] Schlemper J, Caballero J, Hajnal J V et al. A deep cascade of convolutional neural networks for dynamic MR image reconstruction[J]. IEEE Transactions on Medical Imaging, 37, 491-503(2018).

    [24] Liew S L, Anglin J M, Banks N W et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations[J]. Scientific Data, 5, 180011(2018).

    Yu Zhang, Haoran Li, Cheng Li, Fei Li, Shanshan Wang. Combinatorial Reconstruction and Segmentation of Magnetic Resonance Image Using Teacher Forcing[J]. Laser & Optoelectronics Progress, 2022, 59(14): 1415024
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