A novel end-to-end brain tumor segmentation method using improved fully convolutional networks

Computers in Biology and Medicine - Tập 108 - Trang 150-160 - 2019
Haichun Li1, Ao Li1, Minghui Wang1
1School of Information Science and Technology, and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, AH 230027, China

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Tài liệu tham khảo

Soltaninejad, 2017, Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI, Int. J. Comput. Assist. Radiol. Surg., 12, 183, 10.1007/s11548-016-1483-3

Soltaninejad, 2018, Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels, Comput. Methods Progr. Biomed., 157, 69, 10.1016/j.cmpb.2018.01.003

Bauer, 2013, A survey of MRI-based medical image analysis for brain tumor studies, Phys. Med. Biol., 58, R97, 10.1088/0031-9155/58/13/R97

Cui, 2018, Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network, J. Healthc. Eng., 2018, 10.1155/2018/4940593

Tabatabai, 2010, Molecular diagnostics of gliomas: the clinical perspective, Acta Neuropathol. (Berl.), 120, 585, 10.1007/s00401-010-0750-6

Cuadra, 2004, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imaging, 23, 1301, 10.1109/TMI.2004.834618

Prastawa, 2004, A brain tumor segmentation framework based on outlier detection, Med. Image Anal., 8, 275, 10.1016/j.media.2004.06.007

Menze, 2010, A generative model for brain tumor segmentation in multi-modal images, 151

Kleesiek, 2014, Ilastik for multi-modal brain tumor segmentation, Proc. MICCAI BraTS Brain Tumor Segmentation Chall, 12

Bauer, 2011, Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization, 354

Goetz, 2014, Extremely randomized trees based brain tumor segmentation, 006

Meier, 2014, Appearance-and context-sensitive features for brain tumor segmentation, Proc. MICCAI BRATS Chall, 020

Schmidt, 2005, Segmenting brain tumors using alignment-based features, 6

Soltaninejad, 2018, MRI brain tumor segmentation and patient survival prediction using random forests and fully convolutional networks, 204

Soltaninejad, 2014, vol. 6

Subbanna, 2013, Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes, 751

Kamnitsas, 2017, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Med. Image Anal., 36, 61, 10.1016/j.media.2016.10.004

Dvorak, 2015, 13

Pereira, 2016, Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Trans. Med. Imaging, 35, 1240, 10.1109/TMI.2016.2538465

Havaei, 2017, Brain tumor segmentation with deep neural networks, Med. Image Anal., 35, 18, 10.1016/j.media.2016.05.004

Ronneberger, 2015, U-net: convolutional networks for biomedical image segmentation, 234

Xue, 2018, Segan: adversarial network with multi-scale l 1 loss for medical image segmentation, Neuroinformatics, 1

Long, 2015, Fully convolutional networks for semantic segmentation, 3431

Beers, 2017

Dong, 2017, Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks, 506

Erden, 2018

Liu, 2018, Photographic image synthesis with improved U-net, 402

Chen, 2018, DRINet for medical image segmentation, IEEE Trans. Med. Imaging, 37, 2453, 10.1109/TMI.2018.2835303

Szegedy, 2015, Going deeper with convolutions, 1

Menze, 2015, The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. Med. Imaging, 34, 1993, 10.1109/TMI.2014.2377694

Bakas, 2017, Advancing the Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features, Sci. Data., 4, 170117, 10.1038/sdata.2017.117

Ioffe, 2015

Tajbakhsh, 2016, Convolutional neural networks for medical image analysis: full training or fine tuning?, IEEE Trans. Med. Imaging, 35, 1299, 10.1109/TMI.2016.2535302

Jaccard, 1912, The distribution of the flora in the alpine zone. 1, New Phytol., 11, 37, 10.1111/j.1469-8137.1912.tb05611.x

Taha, 2015, Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool, BMC Med. Imaging, 15, 29, 10.1186/s12880-015-0068-x

Chen, 2014, Deep learning-based classification of hyperspectral data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 2094, 10.1109/JSTARS.2014.2329330

Badrinarayanan, 2015

Zhao, 2016

Çiçek, 2016

Li, 2018, Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images, Neuroimage, 183, 650, 10.1016/j.neuroimage.2018.07.005

Will, 2014, Automated segmentation and volumetric analysis of renal cortex, medulla, and pelvis based on non-contrast-enhanced T1- and T2-weighted MR images, Magn. Reson. Mater. Phys. Biol. Med., 27, 445, 10.1007/s10334-014-0429-4

Zhao, 2018, A deep learning model integrating FCNNs and CRFs for brain tumor segmentation, Med. Image Anal., 43, 98, 10.1016/j.media.2017.10.002

Sabour, 2017, Dynamic routing between capsules, 3856

Wang, 2018, Non-local neural networks