A novel end-to-end brain tumor segmentation method using improved fully convolutional networks
Tóm tắt
Từ khóa
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