DeepHarmony: A deep learning approach to contrast harmonization across scanner changes.

[1]  Shunxing Bao,et al.  3D whole brain segmentation using spatially localized atlas network tiles , 2019, NeuroImage.

[2]  Aaron Carass,et al.  Evaluating the Impact of Intensity Normalization on MR Image Synthesis , 2018, Medical Imaging: Image Processing.

[3]  Peter A. Calabresi,et al.  A Deep Learning Based Anti-aliasing Self Super-Resolution Algorithm for MRI , 2018, MICCAI.

[4]  Dinggang Shen,et al.  Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer's Disease Diagnosis , 2018, MICCAI.

[5]  Peter A. Calabresi,et al.  Deep Harmonization of Inconsistent MR Data for Consistent Volume Segmentation , 2018, SASHIMI@MICCAI.

[6]  Jerry L. Prince,et al.  Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size , 2018, SASHIMI@MICCAI.

[7]  Sotirios A. Tsaftaris,et al.  Multimodal MR Synthesis via Modality-Invariant Latent Representation , 2018, IEEE Transactions on Medical Imaging.

[8]  Christos Davatzikos,et al.  Longitudinally and inter-site consistent multi-atlas based parcellation of brain anatomy using harmonized atlases , 2018, NeuroImage.

[9]  Alejandro F. Frangi,et al.  Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 , 2018, Lecture Notes in Computer Science.

[10]  Aaron Carass,et al.  Whole Brain Segmentation and Labeling from CT Using Synthetic MR Images , 2017, MLMI@MICCAI.

[11]  D. Reich,et al.  Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis , 2017, American Journal of Neuroradiology.

[12]  Snehashis Roy,et al.  Cross contrast multi‐channel image registration using image synthesis for MR brain images , 2017, Medical Image Anal..

[13]  Snehashis Roy,et al.  Robust skull stripping using multiple MR image contrasts insensitive to pathology , 2017, NeuroImage.

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

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

[16]  Snehashis Roy,et al.  Random forest regression for magnetic resonance image synthesis , 2017, Medical Image Anal..

[17]  Anisha Keshavan,et al.  Intra- and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis , 2016, NeuroImage.

[18]  Vincent Dumoulin,et al.  Deconvolution and Checkerboard Artifacts , 2016 .

[19]  Aaron Carass,et al.  Consistent cortical reconstruction and multi-atlas brain segmentation , 2016, NeuroImage.

[20]  Russell T. Shinohara,et al.  Removing inter-subject technical variability in magnetic resonance imaging studies , 2016, NeuroImage.

[21]  Shaohua Kevin Zhou,et al.  Unsupervised Cross-Modal Synthesis of Subject-Specific Scans , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  Rohit Bakshi,et al.  Using multiple imputation to efficiently correct cerebral MRI whole brain lesion and atrophy data in patients with multiple sclerosis , 2015, NeuroImage.

[23]  Snehashis Roy,et al.  MR image synthesis by contrast learning on neighborhood ensembles , 2015, Medical Image Anal..

[24]  Snehashis Roy,et al.  Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation , 2015, IEEE Journal of Biomedical and Health Informatics.

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

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

[27]  C. Crainiceanu,et al.  Statistical normalization techniques for magnetic resonance imaging , 2014, NeuroImage: Clinical.

[28]  J. Gee,et al.  The Insight ToolKit image registration framework , 2014, Front. Neuroinform..

[29]  C. Crainiceanu,et al.  Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling☆ , 2013, NeuroImage: Clinical.

[30]  Peter A. Calabresi,et al.  Intensity standardization of longitudinal images using 4D clustering , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[31]  Paul A. Yushkevich,et al.  Multi-Atlas Segmentation with Joint Label Fusion , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  M. Battaglini,et al.  Evaluating and reducing the impact of white matter lesions on brain volume measurements , 2012, Human brain mapping.

[33]  Paul M. Thompson,et al.  Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods , 2011, IEEE Transactions on Medical Imaging.

[34]  D. Louis Collins,et al.  Evaluating intensity normalization on MRIs of human brain with multiple sclerosis , 2011, Medical Image Anal..

[35]  Claude Lepage,et al.  Mapping reliability in multicenter MRI: Voxel‐based morphometry and cortical thickness , 2010, Human brain mapping.

[36]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[37]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[38]  Arthur W. Toga,et al.  Impact of acquisition protocols and processing streams on tissue segmentation of T1 weighted MR images , 2006, NeuroImage.

[39]  L G Nyúl,et al.  On standardizing the MR image intensity scale , 1999, Magnetic resonance in medicine.