Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors

[1]  Snehashis Roy,et al.  Synthesizing CT from Ultrashort Echo-Time MR Images via Convolutional Neural Networks , 2017, SASHIMI@MICCAI.

[2]  Young Jae Kim,et al.  Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study , 2017, PloS one.

[3]  T. Naidich,et al.  Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial , 2017, American Journal of Neuroradiology.

[4]  Nir Shavit,et al.  Deep Learning is Robust to Massive Label Noise , 2017, ArXiv.

[5]  Xiao Han,et al.  MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.

[6]  M. Weller,et al.  Diagnosis and treatment of brain metastases from solid tumors: guidelines from the European Association of Neuro-Oncology (EANO) , 2017, Neuro-oncology.

[7]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Dinggang Shen,et al.  Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features , 2016, LABELS/DLMIA@MICCAI.

[9]  Eudocia Q Lee,et al.  Updates in the management of brain metastases. , 2016, Neuro-oncology.

[10]  J. Lee,et al.  Improved motion-sensitized driven-equilibrium preparation for 3D turbo spin echo T1 weighted imaging after gadolinium administration for the detection of brain metastases on 3T MRI. , 2016, The British journal of radiology.

[11]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

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

[13]  Yann LeCun,et al.  Deep multi-scale video prediction beyond mean square error , 2015, ICLR.

[14]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Christian Szegedy,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[16]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[17]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[18]  Joan Bruna,et al.  Training Convolutional Networks with Noisy Labels , 2014, ICLR 2014.

[19]  Eung Yeop Kim,et al.  Detection of Small Metastatic Brain Tumors: Comparison of 3D Contrast-Enhanced Whole-Brain Black-Blood Imaging and MP-RAGE Imaging , 2012, Investigative radiology.

[20]  K. Yamashita,et al.  3D Turbo Spin-Echo Sequence with Motion-Sensitized Driven-Equilibrium Preparation for Detection of Brain Metastases on 3T MR Imaging , 2011, American Journal of Neuroradiology.

[21]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[22]  Chun Yuan,et al.  Enhanced image quality in black‐blood MRI using the improved motion‐sensitized driven‐equilibrium (iMSDE) sequence , 2010, Journal of magnetic resonance imaging : JMRI.

[23]  S. Higano,et al.  Usefulness of Contrast-Enhanced T1-Weighted Sampling Perfection with Application-Optimized Contrasts by Using Different Flip Angle Evolutions in Detection of Small Brain Metastasis at 3T MR Imaging: Comparison with Magnetization-Prepared Rapid Acquisition of Gradient Echo Imaging , 2009, American Journal of Neuroradiology.

[24]  T. Nagaoka,et al.  Gadolinium-enhanced three-dimensional magnetization-prepared rapid gradient-echo (3D mp-rage) imaging is superior to spin-echo imaging in delineating brain metastases , 2008, Acta radiologica.

[25]  Maxime Descoteaux,et al.  A geometric flow for segmenting vasculature in proton-density weighted MRI , 2008, Medical Image Anal..

[26]  Mitsuru Ikeda,et al.  Contrast-enhanced MR imaging of metastatic brain tumor at 3 tesla: utility of T(1)-weighted SPACE compared with 2D spin echo and 3D gradient echo sequence. , 2008, Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine.

[27]  Dev P Chakraborty,et al.  Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method. , 2006, Academic radiology.

[28]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[29]  Dev P Chakraborty,et al.  Observer studies involving detection and localization: modeling, analysis, and validation. , 2004, Medical physics.

[30]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

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

[32]  Matthew J. McAuliffe,et al.  Medical Image Processing, Analysis and Visualization in clinical research , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[33]  Alejandro F. Frangi,et al.  Quantitative analysis of vascular morphology from 3D MR angiograms: In vitro and in vivo results , 2001, Magnetic resonance in medicine.

[34]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[36]  Yaozong Gao,et al.  Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model , 2016, IEEE Transactions on Medical Imaging.

[37]  Max Losch,et al.  Detection and Segmentation of Brain Metastases with Deep Convolutional Networks , 2015 .

[38]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[39]  S. Eiho,et al.  BRANCH-BASED REGION GROWING METHOD FOR BLOOD VESSEL SEGMENTATION , 2004 .