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Computational Intelligence and Neuroscience
Volume 2015, Article ID 813696, 16 pages
http://dx.doi.org/10.1155/2015/813696
Research Article

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

1Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
2Philips Healthcare, 5680 DA Best, Netherlands
3Faculty of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands
4Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
5Department of Radiology, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
6BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
7Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC, 3015 CN Rotterdam, Netherlands
8Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark
9Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
10Department of Electronics and Communication Engineering, The LNM Institute of Information Technology, Jaipur 302031, India
11Imaging Laboratories, Robarts Research Institute, London, ON, Canada N6A 5B7
12Department of Medical Biophysics, Western University, London, ON, Canada N6A 3K7
13Signals and Systems, Chalmers University of Technology, 41296 Gothenburg, Sweden
14Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723, USA
15Department of Electronics, University of Minho, 4800-058 Guimarães, Portugal
16Department of Computing, Imperial College London, London SW7 2AZ, UK
17Computer Science and Engineering Department, State University of New York at Buffalo, Buffalo, NY 14260-2500, USA
18Center for Medical Imaging Science and Visualization, Linköping University, 58185 Linköping, Sweden
19Department of Radiology and Department of Medical and Health Sciences, Linköping University, 58185 Linköping, Sweden

Received 10 July 2015; Accepted 19 August 2015

Academic Editor: Jussi Tohka

Copyright © 2015 Adriënne M. Mendrik et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [70 citations]

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  • Amarjot Singh, Devamanyu Hazarika, and Aniruddha Bhattacharya, “Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) for Brain Matter Segmentation,” 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1181–1188, . View at Publisher · View at Google Scholar
  • Yongchao Xu, Thierry Geraud, and Isabelle Bloch, “From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning,” 2017 IEEE International Conference on Image Processing (ICIP), pp. 4417–4421, . View at Publisher · View at Google Scholar
  • Kai Xie, and Ying Wen, “LSTM-MA: A LSTM Method with Multi-Modality and Adjacency Constraint for Brain Image Segmentation,” 2019 IEEE International Conference on Image Processing (ICIP), pp. 240–244, . View at Publisher · View at Google Scholar
  • Jinjin Hai, Jian Chen, Kai Qiao, Lei Zeng, Jingbo Xu, and Bin Yan, “Fast medical image segmentation based on patch sharing,” 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 336–340, . View at Publisher · View at Google Scholar
  • Alex Fedorov, Jeremy Johnson, Eswar Damaraju, Alexei Ozerin, Vince Calhoun, and Sergey Plis, “End-to-end learning of brain tissue segmentation from imperfect labeling,” 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3785–3792, . View at Publisher · View at Google Scholar
  • W.M. Kouw, M. Loog, L.W. Bartels, and A.M. Mendrik, “Learning An Mr Acquisition-Invariant Representation Using Siamese Neural Networks,” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 364–367, . View at Publisher · View at Google Scholar
  • Zaineb B. Messaoud, Siwar Chnitti, and Ines Njeh, “An automated MRI brain tissue segmentation approach,” 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 370–374, . View at Publisher · View at Google Scholar
  • Fangzhao Li, Yuxing Peng, Chao Lai, and Shiyao Jin, “An improved watershed in the medical image segmentation based on the bi-dimensional ensemble empirical mode decomposition,” 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6, . View at Publisher · View at Google Scholar
  • Simon Andermatt, Simon Pezold, and Philippe Cattin, “Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data,” Deep Learning and Data Labeling for Medical Applications, vol. 10008, pp. 142–151, 2016. View at Publisher · View at Google Scholar
  • Sérgio Pereira, Adriano Pinto, Jorge Oliveira, Adriënne M. Mendrik, José H. Correia, and Carlos A. Silva, “Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields,” Journal of Neuroscience Methods, vol. 270, pp. 111–123, 2016. View at Publisher · View at Google Scholar
  • Wiro J. Niessen, “MR brain image analysis in dementia: From quantitative imaging biomarkers to ageing brain models and imaging genetics,” Medical Image Analysis, 2016. View at Publisher · View at Google Scholar
  • Toan Duc Bui, Chunsoo Ahn, and Jitae Shin, “Unsupervised segmentation of noisy and inhomogeneous images using global region statistics with non-convex regularization,” Digital Signal Processing, 2016. View at Publisher · View at Google Scholar
  • Richard Beare, Joseph Yuan-Mou Yang, Wirginia J. Maixner, A. Simon Harvey, Michael J. Kean, Vicki A. Anderson, and Marc L. Seal, “Automated alignment of perioperative MRI scans: A technical note and application in pediatric epilepsy surgery,” Human Brain Mapping, 2016. View at Publisher · View at Google Scholar
  • Pim Moeskops, Max A. Viergever, Adrienne M. Mendrik, Linda S. de Vries, Manon J. N. L. Benders, and Ivana Isgum, “Automatic Segmentation of MR Brain Images With a Convolutional Neural Network,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1252–1261, 2016. View at Publisher · View at Google Scholar
  • Dana L. Tudorascu, Helmet T. Karim, Jacob M. Maronge, Lea Alhilali, Saeed Fakhran, Howard J. Aizenstein, John Muschelli, and Ciprian M. Crainiceanu, “Reproducibility and Bias in Healthy Brain Segmentation: Comparison of Two Popular Neuroimaging Platforms,” Frontiers in Neuroscience, vol. 10, 2016. View at Publisher · View at Google Scholar
  • Snehashis Roy, John A. Butman, and Dzung L. Pham, “Robust Skull Stripping Using Multiple MR Image Contrasts Insensitive to Pathology,” NeuroImage, 2016. View at Publisher · View at Google Scholar
  • Rutger Heinen, Willem H. Bouvy, Adrienne M. Mendrik, Max A. Viergever, Geert Jan Biessels, and Jeroen de Bresser, “Robustness of Automated Methods for Brain Volume Measurements across Different MRI Field Strengths,” Plos One, vol. 11, no. 10, pp. e0165719, 2016. View at Publisher · View at Google Scholar
  • Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, and Keum-Shik Hong, “Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface,” Computational Intelligence and Neuroscience, vol. 2016, pp. 1–11, 2016. View at Publisher · View at Google Scholar
  • Hugo J. Kuijf, Pim Moeskops, Bob D. De Vos, Willem H. Bouvy, Jeroen De Bresser, Geert Jan Biessels, Max A. Viergever, and Koen L. Vincken, “Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities,” Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9784, 2016. View at Publisher · View at Google Scholar
  • Hugo J. Kuijf, Neda Sepasian, Nicola Pezzotti, Willem H. Bouvy, Renata Georgia Raidou, Marcel Breeuwer, and Anna Vilanova, “Employing visual analytics to aid the design of white matter hyperintensity classifiers,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901, pp. 97–105, 2016. View at Publisher · View at Google Scholar
  • M.W. Vernooij, M. de Groot, and D. Bos, “Population imaging in neuroepidemiology,” Neuroepidemiology, vol. 138, pp. 69–90, 2016. View at Publisher · View at Google Scholar
  • Boris Kodner, Shiri Gordon, Jacob Goldberger, and Tammy Riklin Raviv, “Atlas of Classifiers for Brain MRI Segmentation,” Machine Learning in Medical Imaging, vol. 10541, pp. 36–44, 2017. View at Publisher · View at Google Scholar
  • Liping Wang, Frédéric Labrosse, and Reyer Zwiggelaar, “Comparison of image intensity, local, and multi-atlas priors in brain tissue classification,” Medical Physics, 2017. View at Publisher · View at Google Scholar
  • Thanh Vân Phan, Dirk Smeets, Joel B. Talcott, and Maaike Vandermosten, “Processing of structural neuroimaging data in young children: bridging the gap between current practice and state-of-the-art methods,” Developmental Cognitive Neuroscience, 2017. View at Publisher · View at Google Scholar
  • Uran Ferizi, Benoit Scherrer, Torben Schneider, Mohammad Alipoor, Odin Eufracio, Rutger H.J. Fick, Rachid Deriche, Markus Nilsson, Ana K. Loya-Olivas, Mariano Rivera, Dirk H.J. Poot, Alonso Ramirez-Manzanares, Jose L. Marroquin, Ariel Rokem, Christian P?tter, Robert F. Dougherty, Ken Sakaie, Claudia Wheeler-Kingshott, Simon K. Warfield, Thomas Witzel, Lawrence L. Wald, Jos? G. Raya, and Daniel C. Alexander, “Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison,” NMR in Biomedicine, pp. e3734, 2017. View at Publisher · View at Google Scholar
  • Roberto Viviani, Eberhard D. Pracht, Daniel Brenner, Petra Beschoner, Julia C. Stingl, and Tony Stöcker, “Multimodal MEMPRAGE, FLAIR, and R2* Segmentation to Resolve Dura and Vessels from Cortical Gray Matter,” Frontiers in Neuroscience, vol. 11, 2017. View at Publisher · View at Google Scholar
  • Jooske Marije Funke Boomsma, Lieza Geertje Exalto, Frederik Barkhof, Esther van den Berg, Jeroen de Bresser, Rutger Heinen, Huiberdina Lena Koek, Niels Daniël Prins, Philip Scheltens, Henry Chanoch Weinstein, Wiesje Maria van der Flier, and Geert Jan Biessels, “Vascular Cognitive Impairment in a Memory Clinic Population: Rationale and Design of the “Utrecht-Amsterdam Clinical Features and Prognosis in Vascular Cognitive Impairment” (TRACE-VCI) Study,” JMIR Research Protocols, vol. 6, no. 4, pp. e60, 2017. View at Publisher · View at Google Scholar
  • Hao Chen, Qi Dou, Lequan Yu, Jing Qin, and Pheng-Ann Heng, “VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images,” NeuroImage, 2017. View at Publisher · View at Google Scholar
  • Mestre Joao, Sergio Pereira, Carlos A. Silva, and Rasteiro, “Modelling brain tissues intensities using dirichlet process,” ENBENG 2017 - 5th Portuguese Meeting on Bioengineering, Proceedings, 2017. View at Publisher · View at Google Scholar
  • Rashindra Manniesing, Bram van Ginneken, Marcel T H Oei, Luuk J Oostveen, Jaime Melendez, Ewoud J Smit, Bram Platel, Clara I Sánchez, Frederick J A Meijer, and Mathias Prokop, “White Matter and Gray Matter Segmentation in 4D Computed Tomography.,” Scientific reports, vol. 7, no. 1, pp. 119, 2017. View at Publisher · View at Google Scholar
  • Amirreza Mahbod, Manish Chowdhury, Örjan Smedby, and Chunliang Wang, “Automatic Brain Segmentation Using Artificial Neural Networks with Shape Context,” Pattern Recognition Letters, 2017. View at Publisher · View at Google Scholar
  • Aaron Carass, Snehashis Roy, Amod Jog, Jennifer L. Cuzzocreo, Elizabeth Magrath, Adrian Gherman, Julia Button, James Nguyen, Ferran Prados, Carole H. Sudre, Manuel Jorge Cardoso, Niamh Cawley, Olga Ciccarelli, Claudia A.M. Wheeler-Kingshott, Sébastien Ourselin, Laurence Catanese, Hrishikesh Deshpande, Pierre Maurel, Olivier Commowick, Christian Barillot, Xavier Tomas-Fernandez, Simon K. Warfield, Suthirth Vaidya, Abhijith Chunduru, Ramanathan Muthuganapathy, Ganapathy Krishnamurthi, Andrew Jesson, Tal Arbel, Oskar Maier, Heinz Handels, Leonardo O. Iheme, Devrim Unay, Saurabh Jain, Diana M. Sima, Dirk Smeets, Mohsen Ghafoorian, Bram Platel, Ariel Birenbaum, Hayit Greenspan, Pierre-Louis Bazin, Peter A. Calabresi, Ciprian M. Crainiceanu, Lotta M. Ellingsen, Daniel S. Reich, Jerry L. Prince, and Dzung L. Pham, “Longitudinal multiple sclerosis lesion segmentation: resource & challenge,” NeuroImage, 2017. View at Publisher · View at Google Scholar
  • Sergi Valverde, Arnau Oliver, Eloy Roura, Sandra González-Villà, Deborah Pareto, Joan C. Vilanova, Lluís Ramió-Torrentà, Àlex Rovira, and Xavier Lladó, “Automated tissue segmentation of MR brain images in the presence of white matter lesions,” Medical Image Analysis, vol. 35, pp. 446–457, 2017. View at Publisher · View at Google Scholar
  • Elliott G. Johnson, Joshua K. Lee, and Simona Ghettipp. 141–166, 2017. View at Publisher · View at Google Scholar
  • Mitko Veta, Maxime W. Lafarge, Pim Moeskops, Koen A. J. Eppenhof, and Josien P. W. Pluim, “Adversarial training and dilated convolutions for brain MRI segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10553, pp. 56–64, 2017. View at Publisher · View at Google Scholar
  • Jose Bernal, Kaisar Kushibar, Daniel S. Asfaw, Sergi Valverde, Arnau Oliver, Robert Martí, and Xavier Lladó, “Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review,” Artificial Intelligence in Medicine, 2018. View at Publisher · View at Google Scholar
  • Aaron Carass, Jennifer L. Cuzzocreo, Shuo Han, Carlos R. Hernandez-Castillo, Paul E. Rasser, Melanie Ganz, Vincent Beliveau, Jose Dolz, Ismail Ben Ayed, Christian Desrosiers, Benjamin Thyreau, José E. Romero, Pierrick Coupé, José V. Manjón, Vladimir S. Fonov, D. Louis Collins, Sarah H. Ying, Chiadi U. Onyike, Deana Crocetti, Bennett A. Landman, Stewart H. Mostofsky, Paul M. Thompson, and Jerry L. Prince, “Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images,” NeuroImage, 2018. View at Publisher · View at Google Scholar
  • Annegreet van Opbroek, Hakim C. Achterberg, Meike W. Vernooij, M. Arfan Ikram, and Marleen de Bruijne, “Transfer learning by feature-space transformation: A method for Hippocampus segmentation across scanners,” NeuroImage: Clinical, 2018. View at Publisher · View at Google Scholar
  • Ilse M.J. Kant, Jeroen de Bresser, Simone J.T. van Montfort, Ellen Aarts, Jorrit-Jan Verlaan, Norman Zacharias, Georg Winterer, Claudia Spies, Arjen J.C. Slooter, and Jeroen Hendrikse, “The association between brain volume, cortical brain infarcts and physical frailty,” Neurobiology of Aging, 2018. View at Publisher · View at Google Scholar
  • Loredana Storelli, Maria A. Rocca, Elisabetta Pagani, Wim Van Hecke, Mark A. Horsfield, Nicola De Stefano, Alex Rovira, Jaume Sastre-Garriga, Jacqueline Palace, Diana Sima, Dirk Smeets, and Massimo Filippi, “Measurement of Whole-Brain and Gray Matter Atrophy in Multiple Sclerosis: Assessment with MR Imaging,” Radiology, pp. 172468, 2018. View at Publisher · View at Google Scholar
  • Lisa A. van der Kleij, Jeroen de Bresser, Jeroen Hendrikse, Jeroen C. W. Siero, Esben T. Petersen, and Jill B. De Vis, “Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T1-based brain segmentation methods,” Plos One, vol. 13, no. 4, pp. e0196119, 2018. View at Publisher · View at Google Scholar
  • Jeffrey P. Guenette, Robert A. Stern, Yorghos Tripodis, Alicia S. Chua, Vivian Schultz, Valerie J. Sydnor, Nathaniel Somes, Sarina Karmacharya, Christian Lepage, Pawel Wrobel, Michael Alosco, Brett M. Martin, Christine E. Chaisson, Michael J. Coleman, Alexander P. Lin, Ofer Pasternak, Nikos Makris, Martha E. Shenton, and Inga K. Koerte, “Automated versus manual segmentation of brain region volumes in former football players,” NeuroImage: Clinical, 2018. View at Publisher · View at Google Scholar
  • Yishi Wang, Yajie Wang, Zhe Zhang, Yuhui Xiong, Qiang Zhang, Chun Yuan, and Hua Guo, “Segmentation of gray matter, white matter, and CSF with fluid and white matter suppression using MP2RAGE,” Journal of Magnetic Resonance Imaging, 2018. View at Publisher · View at Google Scholar
  • Nicholas C. Cullen, and Brian B. Avants, “Convolutional Neural Networks for Rapid and Simultaneous Brain Extraction and Tissue Segmentation,” Brain Morphometry, vol. 136, pp. 13–34, 2018. View at Publisher · View at Google Scholar
  • Robert Dahnke, and Christian Gaser, “Surface and Shape Analysis,” Brain Morphometry, vol. 136, pp. 51–73, 2018. View at Publisher · View at Google Scholar
  • Zhiwen Fan, Liyan Sun, Xinghao Ding, Yue Huang, Congbo Cai, and John Paisley, “A Segmentation-Aware Deep Fusion Network for Compressed Sensing MRI,” Computer Vision – ECCV 2018, vol. 11210, pp. 55–70, 2018. View at Publisher · View at Google Scholar
  • Katarina Fink, Chunjie Guo, Daniel Ferreira, Eric Westman, and Tobias Granberg, “Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis,” European Radiology, 2018. View at Publisher · View at Google Scholar
  • Pim Moeskops, Jeroen de Bresser, Hugo J Kuijf, Adriënne M Mendrik, Geert Jan Biessels, Josien P W Pluim, and Ivana Išgum, “Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.,” NeuroImage. Clinical, vol. 17, pp. 251–262, 2018. View at Publisher · View at Google Scholar
  • Beibei Hou, Guixia Kang, Ningbo Zhang, and Chuan Hu, “Robust 3D Convolutional Neural Network with Boundary Correction for Accurate Brain TissueSegmentation,” IEEE Access, pp. 1–1, 2018. View at Publisher · View at Google Scholar
  • Li Wang, Dong Nie, Guannan Li, Elodie Puybareau, Jose Dolz, Qian Zhang, Fan Wang, Jing Xia, Zhengwang Wu, Jia-Wei Chen, Kim-Han Thung, Toan Duc Bui, Jitae Shin, Guodong Zeng, Guoyan Zheng, Vladimir S. Fonov, Andrew Doyle, Yongchao Xu, Pim Moeskops, Josien P. W. Pluim, Christian Desrosiers, Ismail Ben Ayed, Gerard Sanroma, Oualid M. Benkarim, Adria Casamitjana, Veronica Vilaplana, Weili Lin, Gang Li, and Dinggang Shen, “Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge,” IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2219–2230, 2019. View at Publisher · View at Google Scholar
  • Toan Duc Bui, Jitae Shin, and Taesup Moon, “Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation,” Biomedical Signal Processing and Control, vol. 54, pp. 101613, 2019. View at Publisher · View at Google Scholar
  • Tongxue Zhou, Su Ruan, and Stéphane Canu, “A review: Deep learning for medical image segmentation using multi-modality fusion,” Array, pp. 100004, 2019. View at Publisher · View at Google Scholar
  • Hrvoje Bogunovic, Freerk Venhuizen, Sophie Klimscha, Stefanos Apostolopoulos, Alireza Bab-Hadiashar, Ulas Bagci, Mirza Faisal Beg, Loza Bekalo, Qiang Chen, Carlos Ciller, Karthik Gopinath, Amirali K. Gostar, Kiwan Jeon, Zexuan Ji, Sung Ho Kang, Dara D. Koozekanani, Donghuan Lu, Dustin Morley, Keshab K. Parhi, Hyoung Suk Park, Abdolreza Rashno, Marinko Sarunic, Saad Shaikh, Jayanthi Sivaswamy, Ruwan Tennakoon, Shivin Yadav, Sandro De Zanet, Sebastian M. Waldstein, Bianca S. Gerendas, Caroline Klaver, Clara I. Sanchez, and Ursula Schmidt-Erfurth, “RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge,” IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1858–1874, 2019. View at Publisher · View at Google Scholar
  • Liyan Sun, Zhiwen Fan, Xinghao Ding, Yue Huang, and John Paisley, “Region-of-interest undersampled MRI reconstruction: A deep convolutional neural network approach,” Magnetic Resonance Imaging, 2019. View at Publisher · View at Google Scholar
  • Daniyal Kazempour, Anna Beer, and Thomas Seidl, “Data on RAILs: On Interactive Generation of Artificial Linear Correlated Data,” HCI International 2019 - Posters, vol. 1033, pp. 184–189, 2019. View at Publisher · View at Google Scholar
  • Xiangbo Lin, and Xiaoxi Li, “Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning,” Current Medical Imaging Formerly Current Medical Imaging Reviews, vol. 15, no. 5, pp. 443–452, 2019. View at Publisher · View at Google Scholar
  • Jose Dolz, Karthik Gopinath, Jing Yuan, Herve Lombaert, Christian Desrosiers, and Ismail Ben Ayed, “HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation,” IEEE Transactions on Medical Imaging, vol. 38, no. 5, pp. 1116–1126, 2019. View at Publisher · View at Google Scholar
  • E. E. de Vries, C. J. H. C. M. van Laarhoven, H. J. Kuijf, C. E. V. B. Hazenberg, J. A. van Herwaarden, M. A. Viergever, and G. J. de Borst, “Volumetric assessment of extracranial carotid artery aneurysms,” Scientific Reports, vol. 9, no. 1, 2019. View at Publisher · View at Google Scholar
  • Wouter M. Kouw, Silas N. Ørting, Jens Petersen, Kim S. Pedersen, and Marleen de Bruijne, “A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation,” Information Processing in Medical Imaging, vol. 11492, pp. 360–371, 2019. View at Publisher · View at Google Scholar
  • Liyan Sun, Zhiwen Fan, Xinghao Ding, Yue Huang, and John Paisley, “Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network,” Information Processing in Medical Imaging, vol. 11492, pp. 492–504, 2019. View at Publisher · View at Google Scholar
  • Nikita Nogovitsyn, Roberto Souza, Meghan Muller, Amelia Srajer, Stefanie Hassel, Stephen R. Arnott, Andrew D. Davis, Geoffrey B. Hall, Jacqueline K. Harris, Mojdeh Zamyadi, Paul D. Metzak, Zahinoor Ismail, Signe L. Bray, Catherine Lebel, Jean M. Addington, Roumen Milev, Kate L. Harkness, Benicio N. Frey, Raymond W. Lam, Stephen C. Strother, Benjamin I. Goldstein, Susan Rotzinger, Sidney H. Kennedy, and Glenda M. MacQueen, “Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants,” NeuroImage, 2019. View at Publisher · View at Google Scholar
  • Ryo Ito, Ken Nakae, Junichi Hata, Hideyuki Okano, and Shin Ishii, “Semi-supervised deep learning of brain tissue segmentation,” Neural Networks, 2019. View at Publisher · View at Google Scholar
  • Ananya Anand, and Namrata Anand, “Fast Brain Volumetric Segmentation from T1 MRI Scans,” Polish River Basins and Lakes – Part II, vol. 87, pp. 402–415, 2019. View at Publisher · View at Google Scholar
  • Jelmer M. Wolterink, “Left ventricle segmentation in the era of deep learning,” Journal of Nuclear Cardiology, 2019. View at Publisher · View at Google Scholar
  • Liyan Sun, Zhiwen Fan, Xueyang Fu, Yue Huang, Xinghao Ding, and John Paisley, “A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction,” IEEE Transactions on Image Processing, vol. 28, no. 12, pp. 6141–6153, 2019. View at Publisher · View at Google Scholar
  • Annegreet Van Opbroek, Hakim C. Achterberg, Meike W. Vernooij, and Marleen De Bruijne, “Transfer learning for image segmentation by combining image weighting and kernel learning,” IEEE Transactions on Medical Imaging, vol. 38, no. 1, pp. 213–224, 2019. View at Publisher · View at Google Scholar
  • Nathaniel Swinburne, and Andrei Holodny, “Neurological Diseases,” Artificial Intelligence in Medical Imaging, pp. 217–230, 2019. View at Publisher · View at Google Scholar
  • Toan Duc Bui, Sang-il Ahn, Yongwoo Lee, and Jitae Shin, “A Skip-Connected 3D DenseNet Networks with Adversarial Training for Volumetric Segmentation,” Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, vol. 11383, pp. 378–384, 2019. View at Publisher · View at Google Scholar
  • Hongwei Li, Andrii Zhygallo, and Bjoern Menze, “Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net,” Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, vol. 11383, pp. 385–393, 2019. View at Publisher · View at Google Scholar
  • Yongpei Zhu, Zicong Zhou, Guojun Liao, Qianxi Yang, and Kehong Yuan, “Effects of Differential Geometry Parameters on Grid Generation and Segmentation of MRI Brain Image,” IEEE Access, vol. 7, pp. 68529–68539, 2019. View at Publisher · View at Google Scholar