Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative
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Stefan Zachow | Alexander Tack | Felix Ambellan | Moritz Ehlke | S. Zachow | M. Ehlke | A. Tack | F. Ambellan
[1] D J Hunter,et al. Summary and recommendations of the OARSI FDA osteoarthritis Assessment of Structural Change Working Group. , 2011, Osteoarthritis and cartilage.
[2] Dagmar Kainmüller. Deformable meshes for medical image segmentation: accurate automatic segmentation of anatomical structures , 2014 .
[3] Felix Eckstein,et al. Quantitative MRI of cartilage and bone: degenerative changes in osteoarthritis , 2006, NMR in biomedicine.
[4] Abdul Rahman Ramli,et al. Review of brain MRI image segmentation methods , 2010, Artificial Intelligence Review.
[5] Klaus H. Maier-Hein,et al. 3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection , 2017, IEEE Transactions on Medical Imaging.
[6] Hans-Peter Meinzer,et al. Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..
[7] Christian Igel,et al. Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network , 2013, MICCAI.
[8] Thomas Lange,et al. Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model , 2007 .
[9] Computer-Assisted Intervention,et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI’99 , 1999, Lecture Notes in Computer Science.
[10] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[11] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[12] Atlanta,et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part I. , 2008, Arthritis and rheumatism.
[13] Ersin Yumer,et al. Convolutional neural networks on surfaces via seamless toric covers , 2017, ACM Trans. Graph..
[14] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[15] S Zachow,et al. Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative. , 2018, Osteoarthritis and cartilage.
[16] Claudia Lindner,et al. Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting. , 2015, IEEE transactions on pattern analysis and machine intelligence.
[17] Stefan Zachow,et al. Model-based Auto-Segmentation of Knee Bones and Cartilage in MRI Data , 2010 .
[18] F. Eckstein,et al. Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment. , 2006, Osteoarthritis and cartilage.
[19] Richard Kijowski,et al. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging , 2018, Magnetic resonance in medicine.
[20] Mads Nielsen,et al. Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative , 2015, Journal of medical imaging.
[21] Martin Styner,et al. Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.
[22] Hariharan Ravishankar,et al. Learning and Incorporating Shape Models for Semantic Segmentation , 2017, MICCAI.
[23] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[24] Simon K. Warfield,et al. Segmentation of Knee Images: A Grand Challenge , 2010 .
[25] Hans-Christian Hege,et al. amira: A Highly Interactive System for Visual Data Analysis , 2005, The Visualization Handbook.
[26] Christoph von Tycowicz,et al. An efficient Riemannian statistical shape model using differential coordinates: With application to the classification of data from the Osteoarthritis Initiative , 2018, Medical Image Anal..
[27] Konstantinos Kamnitsas,et al. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation , 2017, IEEE Transactions on Medical Imaging.
[28] Mattias P. Heinrich,et al. OBELISK - One Kernel to Solve Nearly Everything: Unified 3D Binary Convolutions for Image Analysis , 2018 .
[29] Seyed-Ahmad Ahmadi,et al. Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields , 2016, MICCAI.
[30] S. Gabriel,et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part II. , 2008, Arthritis and rheumatism.
[31] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[32] Daniel Rueckert,et al. Patch-Based Segmentation without Registration: Application to Knee MRI , 2013, MLMI.
[33] Johan Thunberg,et al. Shape‐aware surface reconstruction from sparse 3D point‐clouds , 2016, Medical Image Anal..
[34] H. Lamecker,et al. Segmentation of the Liver using a 3D Statistical Shape Model , 2004 .
[35] Anirban Mukhopadhyay,et al. Robust and Accurate Appearance Models Based on Joint Dictionary Learning Data from the Osteoarthritis Initiative - Data from the Osteoarthritis Initiative , 2016, Patch-MI@MICCAI.