Deep learning-Based 3D inpainting of brain MR images

[1]  Jae Sung Lee A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography , 2021, IEEE Transactions on Radiation and Plasma Medical Sciences.

[2]  Jae Sung Lee,et al.  Measurement of Glomerular Filtration Rate using Quantitative SPECT/CT and Deep-learning-based Kidney Segmentation , 2019, Scientific Reports.

[3]  Jae Sung Lee,et al.  Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps , 2019, The Journal of Nuclear Medicine.

[4]  Jae Sung Lee,et al.  Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning , 2018, The Journal of Nuclear Medicine.

[5]  Jae Sung Lee,et al.  Computed tomography super-resolution using deep convolutional neural network , 2018, Physics in medicine and biology.

[6]  Thomas S. Huang,et al.  Free-Form Image Inpainting With Gated Convolution , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[7]  Dong Young Lee,et al.  Adaptive template generation for amyloid PET using a deep learning approach , 2018, Human brain mapping.

[8]  Ting-Chun Wang,et al.  Image Inpainting for Irregular Holes Using Partial Convolutions , 2018, ECCV.

[9]  Thomas S. Huang,et al.  Generative Image Inpainting with Contextual Attention , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[10]  Sayanti Chaudhuri,et al.  Obstructive sleep apnoea detection using convolutional neural network based deep learning framework , 2017, Biomedical Engineering Letters.

[11]  D. Y. Lee,et al.  Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease: Methodology and Baseline Sample Characteristics , 2017, Psychiatry investigation.

[12]  Andrew P. Leynes,et al.  Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI , 2017, The Journal of Nuclear Medicine.

[13]  Romany F Mansour,et al.  Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy , 2017, Biomedical Engineering Letters.

[14]  A. Strafella,et al.  Imaging biomarkers in Parkinson’s disease and Parkinsonian syndromes: current and emerging concepts , 2017, Translational Neurodegeneration.

[15]  Simon K. Warfield,et al.  A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans , 2017, IEEE Transactions on Medical Imaging.

[16]  Simon K. Warfield,et al.  Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices , 2016, IEEE Journal of Biomedical and Health Informatics.

[17]  Yaozong Gao,et al.  Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks , 2016, LABELS/DLMIA@MICCAI.

[18]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Antonio Greco,et al.  Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer’s disease: a systematic review , 2016, BMC Geriatrics.

[21]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[22]  Juha Koikkalainen,et al.  Differential diagnosis of neurodegenerative diseases using structural MRI data , 2016, NeuroImage: Clinical.

[23]  Yibao Li,et al.  A compact fourth-order finite difference scheme for the three-dimensional Cahn-Hilliard equation , 2016, Comput. Phys. Commun..

[24]  Andrea C. Bozoki,et al.  Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification , 2016, PloS one.

[25]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Sepp Hochreiter,et al.  Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[27]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[28]  H. Möller,et al.  Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. , 2015, Brain : a journal of neurology.

[29]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[30]  Giovanni Montana,et al.  Deep neural networks for anatomical brain segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[31]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Seong-Whan Lee,et al.  Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis , 2014, NeuroImage.

[33]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[34]  Kourosh Jafari-Khouzani,et al.  MRI Upsampling Using Feature-Based Nonlocal Means Approach , 2014, IEEE Transactions on Medical Imaging.

[35]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[36]  Enhong Chen,et al.  Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.

[37]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[38]  Ludwig Kappos,et al.  Multivariate pattern classification of gray matter pathology in multiple sclerosis , 2012, NeuroImage.

[39]  Yoram Bresler,et al.  MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning , 2011, IEEE Transactions on Medical Imaging.

[40]  A. Reiss,et al.  Neuroanatomical spatial patterns in Turner syndrome , 2011, NeuroImage.

[41]  A. Aleman,et al.  Regional brain volume in depression and anxiety disorders. , 2010, Archives of general psychiatry.

[42]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[43]  G. Alexander,et al.  Fibrillar amyloid-β burden in cognitively normal people at 3 levels of genetic risk for Alzheimer's disease , 2009, Proceedings of the National Academy of Sciences.

[44]  Nick C Fox,et al.  Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[45]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[46]  Elizabeth H. Aylward,et al.  Change in MRI striatal volumes as a biomarker in preclinical Huntington's disease , 2007, Brain Research Bulletin.

[47]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[48]  W. Eric L. Grimson,et al.  A Genetic Algorithm for the Topology Correction of Cortical Surfaces , 2005, IPMI.

[49]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[50]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.

[51]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[52]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[53]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[54]  Hayit Greenspan,et al.  MRI Inter-slice Reconstruction Using Super-Resolution , 2001, MICCAI.

[55]  Anders M. Dale,et al.  A hybrid approach to the Skull Stripping problem in MRI , 2001, NeuroImage.

[56]  K O Lim,et al.  Progressive brain volume changes and the clinical course of schizophrenia in men: a longitudinal magnetic resonance imaging study. , 2001, Archives of general psychiatry.

[57]  G. Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[58]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[59]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[60]  A. Ardeshir Goshtasby,et al.  Matching of tomographic slices for interpolation , 1992, IEEE Trans. Medical Imaging.