Knee Meniscus Segmentation and Tear Detection from MRI: A Review.

BACKGROUND Automatic diagnostic systems in medical imaging provide useful information to support radiologists and other relevant experts. The systems that help radiologists in their analysis and diagnosis appear to be increasing. DISCUSSION Knee joints are intensively studied structures, as well. In this review, studies that automatically segment meniscal structures from the knee joint MR images and detect tears have been investigated. Some of the studies in the literature merely perform meniscus segmentation, while others include classification procedures that detect both meniscus segmentation and anomalies on menisci. The studies performed on the meniscus were categorized according to the methods they used. The methods used and the results obtained from such studies were analyzed along with their drawbacks, and the aspects to be developed were also emphasized. CONCLUSION The work that has been done in this area can effectively support the decisions that will be made by radiology and orthopedics specialists. Furthermore, these operations, which were performed manually on MR images, can be performed in a shorter time with the help of computeraided systems, which enables early diagnosis and treatment.

[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.