Knee Meniscus Segmentation and Tear Detection from MRI: A Review.
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[1] S Zachow,et al. Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative. , 2018, Osteoarthritis and cartilage.
[2] Chein-I Chang,et al. An Automatic Computer-Aided Detection System for Meniscal Tears on Magnetic Resonance Images , 2009, IEEE Transactions on Medical Imaging.
[3] Yang-Kun Ou,et al. Computer-aided diagnosis for knee meniscus tears in magnetic resonance imaging , 2013 .
[4] Heye Zhang,et al. Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression , 2017, Medical Image Anal..
[5] Jerry L Prince,et al. Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.
[6] Christopher Joseph Pal,et al. Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..
[7] Hans-Peter Meinzer,et al. Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..
[8] Guido Gerig,et al. A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..
[9] Ronald M. Summers,et al. Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation , 2015, IEEE Transactions on Medical Imaging.
[10] Christian Igel,et al. Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network , 2013, MICCAI.
[11] Gary F. Egan,et al. Vein segmentation using shape-based Markov Random Fields , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[12] Ling Shao,et al. Sub-Markov Random Walk for Image Segmentation , 2016, IEEE Transactions on Image Processing.
[13] Marc Niethammer,et al. Automatic atlas-based three-label cartilage segmentation from MR knee images , 2014, 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis.
[14] Ron Kikinis,et al. Markov random field segmentation of brain MR images , 1997, IEEE Transactions on Medical Imaging.
[15] Voshell Af. Anatomy of the knee joint. , 1956 .
[16] Sang Uk Lee,et al. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques , 1990, Pattern Recognit..
[17] Torsten Rohlfing,et al. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains , 2004, NeuroImage.
[18] J. Fripp,et al. Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images--data from the Osteoarthritis Initiative. , 2014, Osteoarthritis and cartilage.
[19] Stuart Crozier,et al. Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images , 2015, Physics in medicine and biology.
[20] Sankar K. Pal,et al. A review on image segmentation techniques , 1993, Pattern Recognit..
[21] Heye Zhang,et al. A Meshfree Representation for Cardiac Medical Image Computing , 2018, IEEE Journal of Translational Engineering in Health and Medicine.
[22] David Suter,et al. Development of Semi-Automatic Segmentation Methods for Measuring Tibial Cartilage Volume , 2005, Digital Image Computing: Techniques and Applications (DICTA'05).
[23] Mohammad Hossein Fazel Zarandi,et al. A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear , 2016, Journal of Digital Imaging.
[24] Isabelle Bloch,et al. 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models , 2009, Fuzzy Sets Syst..
[25] Luisa P. Wallace,et al. Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. , 2004, Journal of the National Cancer Institute.
[26] Hossein Pourghassem,et al. Content-based medical image classification using a new hierarchical merging scheme , 2008, Comput. Medical Imaging Graph..
[27] Simo Saarakkala,et al. Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach , 2017, Scientific Reports.
[28] H. Haneishi,et al. Relationship between knee osteoarthritis and meniscal shape in observation of Japanese patients by using magnetic resonance imaging , 2017, Journal of Orthopaedic Surgery and Research.
[29] Ron Kikinis,et al. Improved watershed transform for medical image segmentation using prior information , 2004, IEEE Transactions on Medical Imaging.
[30] Hui-Hong Duan,et al. Two-pass region growing combined morphology algorithm for segmenting airway tree from CT chest scans , 2016, 2016 UKACC 11th International Conference on Control (CONTROL).
[31] J Jiang,et al. Medical image analysis with artificial neural networks , 2010, Comput. Medical Imaging Graph..
[32] Felicia Aldrin,et al. Automated Segmentation of the Meniscus , 2017 .
[33] Erik Dam. Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging - with data from the Osteoarthritis Initiative , 2017, ArXiv.
[34] Sanjay N. Talbar,et al. Fully automated subchondral bone segmentation from knee MR images: Data from the Osteoarthritis Initiative , 2017, Comput. Biol. Medicine.
[35] Nadia Magnenat-Thalmann,et al. MRI Bone Segmentation Using Deformable Models and Shape Priors , 2008, MICCAI.
[36] Olivier D. Faugeras,et al. Segmentation of Bone in Clinical Knee MRI Using Texture-Based Geodesic Active Contours , 1998, MICCAI.
[37] Hrvoje Kalinić,et al. Atlas-based image segmentation: A Survey , 2009 .
[38] Rachel K. Surowiec,et al. Automated T2-mapping of the Menisci From Magnetic Resonance Images in Patients with Acute Knee Injury. , 2017, Academic radiology.
[39] 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.
[40] Alan L. Yuille,et al. Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification , 2008, IEEE Transactions on Medical Imaging.
[41] Helen Hong,et al. Automatic Segmentation of the meniscus based on Active Shape Model in MR Images through Interpolated Shape Information , 2010 .
[42] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[43] Anu Mehra,et al. Automatic brain tumor segmentation and extraction in MR images , 2016, 2016 Conference on Advances in Signal Processing (CASP).
[44] E. Panagiotopoulos,et al. A computer-based system for the discrimination between normal and degenerated menisci from Magnetic Resonance Images , 2008, 2008 IEEE International Workshop on Imaging Systems and Techniques.
[45] Carlos Ortiz-de-Solorzano,et al. Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data , 2009, IEEE Transactions on Medical Imaging.
[46] Tran Manh Tuan,et al. A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation , 2016, Expert Syst. Appl..
[47] Songül Albayrak,et al. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling , 2016, Comput. Biol. Medicine.
[48] H.P. Ng,et al. Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm , 2006, 2006 IEEE Southwest Symposium on Image Analysis and Interpretation.
[49] Rolf Adams,et al. Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[50] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.
[51] Yutaka Hata,et al. Computer Aided Diagnosis System of Meniscal Tears with T1 and T2 Weighted MR Images Based on Fuzzy Inference , 2001, Fuzzy Days.
[52] J. Sethian,et al. A Fast Level Set Method for Propagating Interfaces , 1995 .
[53] Cemal Köse,et al. An automatic diagnosis method for the knee meniscus tears in MR images , 2009, Expert Syst. Appl..
[54] Heysem Kaya,et al. Automatic detection of meniscal area in the knee MR images , 2016, 2016 24th Signal Processing and Communication Application Conference (SIU).
[55] Heye Zhang,et al. Unsupervised boundary delineation of spinal neural foramina using a multi‐feature and adaptive spectral segmentation , 2017, Medical Image Anal..
[56] Jianfeng Lu,et al. A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation , 2007, Computing in Science & Engineering.
[57] Jürgen Weese,et al. Shape Constrained Deformable Models for 3D Medical Image Segmentation , 2001, IPMI.
[58] Mithat Gönen,et al. Breast cancer detection and tumor characteristics in BRCA1 and BRCA2 mutation carriers , 2017, Breast Cancer Research and Treatment.
[59] Piotr Kohut,et al. Image processing in detection of knee joints injuries based on MRI images , 2017 .
[60] Stuart Crozier,et al. Automated segmentation of the menisci from MR images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[61] L. Tabár,et al. REDUCTION IN MORTALITY FROM BREAST CANCER AFTER MASS SCREENING WITH MAMMOGRAPHY Randomised Trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare , 1985, The Lancet.
[62] Hamid Jafarkhani,et al. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..
[63] Chun Chen,et al. Surface Rendering for Parallel Slices of Contours from Medical Imaging , 2007, Computing in Science & Engineering.
[64] Demetri Terzopoulos,et al. Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..
[65] Vijay K. Jain,et al. Markov random field for tumor detection in digital mammography , 1995, IEEE Trans. Medical Imaging.
[66] Victor Alves,et al. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.
[67] Chengwen Chu,et al. Multi‐atlas pancreas segmentation: Atlas selection based on vessel structure , 2017, Medical Image Anal..
[68] V. V. Satyanarayana Tallapragada,et al. A NOVEL MEDICAL IMAGE SEGMENTATION AND CLASSIFICATION USING COMBINED FEATURE SET AND DECISION TREE CLASSIFIER , 2015 .
[69] 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.
[70] Ah Chung Tsoi,et al. Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.
[71] Alexandre X Falcão,et al. Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models. , 2015, Medical physics.
[72] M. S. Mallikarjunaswamy,et al. Knee joint menisci visualization and detection of tears by image processing , 2012, 2012 International Conference on Computing, Communication and Applications.
[73] W. Eric L. Grimson,et al. Adaptive Segmentation of MRI Data , 1995, CVRMed.
[74] Yong-woo Lee,et al. Fully automatic segmentation based on localizing active contour method , 2014, ICUIMC.
[75] Yu Xue,et al. Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation , 2017, Appl. Soft Comput..
[76] Devendra Somwanshi,et al. Thresholding and morphological based segmentation techniques for medical images , 2016, 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE).
[77] Nicholas Ayache,et al. A Generative Model for Brain Tumor Segmentation in Multi-Modal Images , 2010, MICCAI.
[78] Jerry L Prince,et al. Image Segmentation Using Deformable Models , 2000 .
[79] Benoit M. Dawant,et al. Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.
[80] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[81] M. Gurcan,et al. Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees. , 2010, Osteoarthritis and cartilage.
[82] Wolfgang Birkfellner,et al. Applied Medical Image Processing: A Basic Course , 2010 .
[83] Demetri Terzopoulos,et al. Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[84] Tzong-Jer Chen,et al. Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..
[85] Ole Fogh Olsen,et al. Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach , 2007, IEEE Transactions on Medical Imaging.
[86] Timothy F. Cootes,et al. Comparing Active Shape Models with Active Appearance Models , 1999, BMVC.
[87] Pina Marziliano,et al. The unified extreme learning machines and discriminative random fields for automatic knee cartilage and meniscus segmentation from multi-contrast MR images , 2012, Machine Vision and Applications.
[88] Martin P. DeSimio,et al. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency , 1997, IEEE Transactions on Medical Imaging.
[89] Petra Macaskill,et al. Breast cancer detection using single-reading of breast tomosynthesis (3D-mammography) compared to double-reading of 2D-mammography: Evidence from a population-based trial. , 2017, Cancer epidemiology.
[90] Josef Kittler,et al. Region growing: a new approach , 1998, IEEE Trans. Image Process..
[91] Jun Chen,et al. Correlated Regression Feature Learning for Automated Right Ventricle Segmentation , 2018, IEEE Journal of Translational Engineering in Health and Medicine.
[92] Songul Albayrak,et al. A new computer-based approach for fully automated segmentation of knee meniscus from magnetic resonance images , 2017 .
[93] J. Bezdek,et al. FCM: The fuzzy c-means clustering algorithm , 1984 .
[94] Songul Albayrak,et al. Meniscus segmentation and tear detection in the knee MR images by fuzzy c-means method , 2017, 2017 25th Signal Processing and Communications Applications Conference (SIU).
[95] Petra Macaskill,et al. Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study. , 2016, The Lancet. Oncology.
[96] Stefan Zachow,et al. Model-based Auto-Segmentation of Knee Bones and Cartilage in MRI Data , 2010 .
[97] Stuart Crozier,et al. Automatic Segmentation and Quantitative Analysis of the Articular Cartilages From Magnetic Resonance Images of the Knee , 2010, IEEE Transactions on Medical Imaging.
[98] Demetri Terzopoulos,et al. Snakes: Active contour models , 2004, International Journal of Computer Vision.
[99] Edwin N. Cook,et al. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks , 1997, IEEE Transactions on Medical Imaging.
[100] Klaus D. Tönnies,et al. Segmentation of medical images using adaptive region growing , 2001, SPIE Medical Imaging.
[101] Guido Gerig,et al. Automatic brain tumor segmentation by subject specific modification of atlas priors. , 2003, Academic radiology.
[102] H. D. de Koning,et al. Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. , 2004, The New England journal of medicine.