Relevant bibliographies by topics / Knee segmentation in MRI

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Academic literature on the topic 'Knee segmentation in MRI'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Knee segmentation in MRI"

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Riza, Sulaiman, Djasmir Marlinawati, and Mohamad Amran Mohd Fahmi. "COMSeg technique for MRI knee cartilage segmentation." International Review of Applied Sciences and Engineering 10, no. 2 (December 2019): 147–55. http://dx.doi.org/10.1556/1848.2019.0018.

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Segmentation is one of important methods in medical images processing, particularly as it allows images to be analysed. The method used for segmentation depends on the image problem to be resolved. In this research, knee cartilage needs to be segmented to determine the level of the Osteoarthritis (OA) and for further treatment. Knee cartilage is a soft hyline sponge that is located at the end of the femur, tibia and patella bone to release friction during movement. OA is a knee cartilage problem wherein there is a thinning of the cartilage that results in a shift especially happening between femur and tibia bone causing discomfort and pain. Thinning of the knee cartilage is due to many factors such as age, body weight, genetic, accident, sport injury and extreme use such as physical work. OA can occur to a male or female, child or adult. The effects experienced by patients with OA are such as difficulty to walk, limited movement, and pain in the thin cartilage areas. Monitoring of patients' condition needs to be done to help reduce the problem and thereby enable specialists to perform the appropriate treatment. Imaging is a method used today to monitor the condition of patients with OA. Previous studies showed that MRI is a suitable method for monitoring the condition of patients with OA because of its advantages in visualising knee cartilage more clearly than other imaging methods. Thus, for segmenting the knee cartilage which as mentioned before is an important process in medical images processing, the MR images were selected based on many factors. Segmentation in this study was aimed to obtain the cartilage region to diagnose patient OA level. Various segmentation techniques have been developed by researchers in segmenting the knee cartilage region but they have been unable to segment precisely due to the thin structure of the knee cartilage, especially for patients with intermediate and severe OA. COMSeg technique was developed to segment knee cartilage, especially for those experiencing a normal and intermediate OA and try to implement it to severe OA. The development of this new technique takes into account the imaging method used, the images feature obtained so it can be suitable to process knee image and then selection of an appropriate technique to be applied to the selected images as input.
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Oei, Edwin H. G., Tijmen A. van Zadelhoff, Susanne M. Eijgenraam, Stefan Klein, Jukka Hirvasniemi, and Rianne A. van der Heijden. "3D MRI in Osteoarthritis." Seminars in Musculoskeletal Radiology 25, no. 03 (June 2021): 468–79. http://dx.doi.org/10.1055/s-0041-1730911.

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AbstractOsteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.
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More, Sujeet, Jimmy Singla, Ahed Abugabah, and Ahmad Ali AlZubi. "Machine Learning Techniques for Quantification of Knee Segmentation from MRI." Complexity 2020 (December 7, 2020): 1–13. http://dx.doi.org/10.1155/2020/6613191.

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Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed.
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Barendregt, Anouk M., Valentina Mazzoli, J. Merlijn van den Berg, Taco W. Kuijpers, Mario Maas, Aart J. Nederveen, and Robert Hemke. "T1ρ-mapping for assessing knee joint cartilage in children with juvenile idiopathic arthritis — feasibility and repeatability." Pediatric Radiology 50, no. 3 (November 9, 2019): 371–79. http://dx.doi.org/10.1007/s00247-019-04557-4.

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Abstract Background Ongoing arthritis in children with juvenile idiopathic arthritis (JIA) can result in cartilage damage. Objective To study the feasibility and repeatability of T1ρ for assessing knee cartilage in JIA and also to describe T1ρ values and study correlation between T1ρ and conventional MRI scores for disease activity. Materials and methods Thirteen children with JIA or suspected JIA underwent 3-tesla (T) knee MRI that included conventional sequences and a T1ρ sequence. Segmentation of knee cartilage was carried out on T1ρ images. We used intraclass correlation coefficient to study the repeatability of segmentation in a subset of five children. We used the juvenile arthritis MRI scoring system to discriminate inflamed from non-inflamed knees. The Mann-Whitney U and Spearman correlation compared T1ρ between children with and without arthritis on MRI and correlated T1ρ with the juvenile arthritis MRI score. Results All children successfully completed the MRI examination. No images were excluded because of poor quality. Repeatability of T1ρ measurement had an intraclass correlation coefficient (ICC) of 0.99 (P<0.001). We observed no structural cartilage damage and found no differences in T1ρ between children with (n=7) and without (n=6) inflamed knees (37.8 ms vs. 31.7 ms, P=0.20). However, we observed a moderate correlation between T1ρ values and the juvenile arthritis MRI synovitis score (r=0.59, P=0.04). Conclusion This pilot study suggests that T1ρ is a feasible and repeatable quantitative imaging technique in children. T1ρ values were associated with the juvenile arthritis MRI synovitis score.
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Zhang, Ying, Mo Ruan, Hongbo Tan, Ming Chen, and Yongqing Xu. "Analysis of the Effect of Intra-Articular Injection of Platelet-Rich Plasma on Knee Arthritis Pain Based on MRI Image Segmentation Algorithm." Journal of Medical Imaging and Health Informatics 11, no. 1 (January 1, 2021): 192–96. http://dx.doi.org/10.1166/jmihi.2021.3441.

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Objective: To study the effect of bone marrow platelet-rich plasma (PRP) injection on osteoarthritis using a level set-based MRI image segmentation algorithm. Methods: 180 patients with knee osteoarthritis (206 knees) were randomly divided into observation group (92 knees) and control group (88 knees). The observation group was injected with 2 mL of enriched stem cells and PRP suspension in the joint cavity, and then injected once again after 6 weeks. The control group was injected with 2 mL of sodium hyaluronate in the joint cavity, once a week for a total of 5 times. After 3 months and 6 months of treatment, the patients were followed up, and the clinical efficacy of the two groups was evaluated by the WOMAC score. The MRI image segmentation of the patients was analyzed using the level set-based MRI image segmentation method. Results: The WOMAC score observation group was 45.88 ± 9.54 points before treatment, 25.26 ± 6.67 points 3 months after treatment, 22.44 ± 5.19 points 6 months after treatment; the control group was 46.76 ± 8.06 points before treatment and 32.12 ± 5.35 3 months after treatment. Points, 33.34 ± 6.32 points 6 months after treatment. The WOMAC score of the observation group and the control group was improved after treatment, and the difference was statistically significant (P < 0.05) compared with that before treatment. Conclusion: The observation group has a clear effect on the treatment of knee osteoarthritis and is worthy of clinical application.
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Kashyap, S., H. Zhang, and M. Sonka. "Accurate Fully Automated 4D Segmentation of Osteoarthritic Knee MRI." Osteoarthritis and Cartilage 25 (April 2017): S227—S228. http://dx.doi.org/10.1016/j.joca.2017.02.391.

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Saygili, Ahmet, and Songül Albayrak. "Knee Meniscus Segmentation and Tear Detection from MRI: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (January 6, 2020): 2–15. http://dx.doi.org/10.2174/1573405614666181017122109.

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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.
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Kumar, Deepak, and Jitendra Bhaskar. "A Review on Modelling of Knee Joint Using Medical Imaging Methods." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 5, no. 4 (October 5, 2020): 84–89. http://dx.doi.org/10.35121/ijapie202001146.

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The accuracy of the 3D CAD model of the knee joint is based on various factors like imaging method i.e CT scan, MRI data, modelling software and different algorithms for segmentation. For generating geometrical and CAD model techniques like CT scan, Co-ordinate Measuring Machine (CMM) and 3D laser scanner is used. So in this paper efforts have been made to study the different factors which affect the accuracy of a 3D CAD and additively manufactured knee model. Accuracy of the knee joint is important for anatomical study, implant modeling, and pre-surgical planning. The segmentation technique is another important factor that affects the accuracy of a 3D CAD model so each segmentation technique has its pros and cons therefore evaluation of segmentation technique is also studied and compared with each other.
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Dam, E. B., and J. Marques. "422 AUTOMATIC SEGMENTATION OF BONE AND CARTILAGE FROM KNEE MRI." Osteoarthritis and Cartilage 19 (September 2011): S196. http://dx.doi.org/10.1016/s1063-4584(11)60449-4.

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Aprovitola, Andrea, and Luigi Gallo. "Knee bone segmentation from MRI: A classification and literature review." Biocybernetics and Biomedical Engineering 36, no. 2 (2016): 437–49. http://dx.doi.org/10.1016/j.bbe.2015.12.007.

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Dissertations / Theses on the topic "Knee segmentation in MRI"

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Lind, Marcus. "Automatic Segmentation of Knee Cartilage Using Quantitative MRI Data." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138403.

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This thesis investigates if support vector machine classification is a suitable approach when performing automatic segmentation of knee cartilage using quantitative magnetic resonance imaging data. The data sets used are part of a clinical project that investigates if patients that have suffered recent knee damage will develop cartilage damage. Therefore the thesis also investigates if the segmentation results can be used to predict the clinical outcome of the patients. Two methods that perform the segmentation using support vector machine classification are implemented and evaluated. The evaluation indicates that it is a good approach for the task, but the implemented methods needs to be further improved and tested on more data sets before clinical use. It was not possible to relate the cartilage properties to clinical outcome using the segmentation results. However, the investigation demonstrated good promise of how the segmentation results, if they are improved, can be used in combination with quantitative magnetic resonance imaging data to analyze how the cartilage properties change over time or vary between knees.
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Kashyap, Satyananda. "Quantitative analysis and segmentation of knee MRI using layered optimal graph segmentation of multiple objects and surfaces." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2228.

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Knee osteoarthritis is one of the most debilitating aging diseases as it causes loss of cartilage of the knee joint. Knee osteoarthritis affects the quality of life and increases the burden on health care costs. With no disease-modifying osteoarthritis drug currently available there is an immediate need to understand the factors triggering the onset and progression of the disease. Developing robust segmentation techniques and quantitative analysis helps identify potential imaging-based biomarkers that indicate the onset and progression of osteoarthritis. This thesis work developed layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) framework based knee MRI segmentation algorithms in 3D and longitudinal 3D (4D). A hierarchical random forest classifier algorithm was developed to improve cartilage costs functions for the LOGISMOS framework. The new cost function design significantly improved the segmentation accuracy over the existing state of the art methods. Disease progression results in more artifacts appearing similar to cartilage in MRI. 4D LOGISMOS segmentation was developed to simultaneously segment multiple time-points of a single patient by incorporating information from earlier time points with a relatively healthier knee in the early stage of the disease. Our experiments showed consistently higher segmentation accuracy across all the time-points over 3D LOGISMOS segmentation of each time-point. Fully automated segmentation algorithms proposed are not 100% accurate especially for patient MRI's having severe osteoarthritis and require interactive correction. An interactive technique called just-enough interaction (JEI) was developed which added a fast correction step to the automated LOGISMOS, speeding up the interactions substantially over the current slice-by-slice manual editing while maintaining high accuracy. JEI editing modifies the graph nodes instead of the boundary surfaces of the bones and cartilages providing globally optimally corrected results. 3D JEI was extended to 4D JEI allowing for simultaneous visualization and interaction of multiple time points of the same patients. Further quantitative analysis tools were developed to study the thickness losses. Nomenclature compliant sub-plate detection algorithm was developed to quantify thickness in the smaller load bearing regions of the knee to help understand the varying rates of thickness losses in the different regions. Regression models were developed to predict the thickness accuracy on a patient MRI at a later follow-up using the available thickness information from the LOGISMOS segmentation of the current set of MRI scans of the patient. Further non-cartilage based imaging biomarker quantification was developed to analyze bone shape changes between progressing and non-progressing osteoarthritic populations. The algorithm quantified statistically significant local shape changes between the two populations. Overall this work improved the state of the art in the segmentation of the bones and cartilage of the femur and tibia. Interactive 3D and 4D JEI were developed allowing for fast corrections of the segmentations and thus significantly improving the accuracy while performing many times faster. Further, the quantitative analysis tools developed robustly analyzed the segmentation providing measurable metrics of osteoarthritis progression.
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Dib, Zoheir. "Chirurgie orthopédique assistée par ordinateur : application au traitement de l'arthrose du genou." Thesis, Brest, 2017. http://www.theses.fr/2017BRES0070/document.

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L’arthrose est un véritable problème de santé publique. Plus de dix millions de personnes sont atteintes en France et 35 millions aux États-Unis. L’arthrose du genou représente 35% du nombre total d’arthrose avec plus de 1,3 million de patients en Europe. Il existe de nos jours plusieurs solutions permettant de traiter l’arthrose du genou, suivant le caractère dégénératif de la maladie, allant du traitement chirurgical conservateur, tel que l’ostéotomie supérieure du tibia, jusqu’au traitement chirurgical prothétique, tel que l’arthroplastie totale du genou. Le succès à long terme de ces interventions repose (1) sur le contrôle de l’alignement du membre inférieur au cours de l’intervention, réalisé par l’intermédiaire de l’angle HKA entre les centres hanche, genou et cheville, et (2) sur une planification chirurgicale permettant de préparer l’intervention, et notamment, définir la position optimale des coupes osseuses pour la mise en place d’une prothèse à partir de modèles 3D de l’os du patient issus d’images tomodensitométriques (TDM) ou IRM. Nous nous sommes intéressés, dans un premier temps, à l’étude et l’évaluation, dans un contexte clinique, de la précision et la robustesse des techniques utilisées en chirurgie assistée par ordinateur pour la localisation du centre hanche, nécessaire au calcul de l’angle HKA. Nous avons ainsi proposé une nouvelle méthode, mini-invasive, et particulièrement adaptée pour l’ostéotomie supérieure du tibia. Nous nous sommes ensuite intéressés aux méthodes de segmentation permettant d’extraire la surface osseuse du genou à partir d’IRM pour la phase de planification. Nous avons également proposé une nouvelle approche, automatique, qui se base sur des modèles actifs de forme ou Active Shape Model (ASM). Compte tenu des résultats très encourageants, l’intégration de nos contributions en routine clinique pourrait, potentiellement, améliorer le service médical rendu pour le traitement de l’arthrose du genou
Osteoarthritis is a real public health problem. More than ten million people are affected by osteoarthritis in France and 35 million in the United States. Knee Osteoarthritis represents 35% of the total number of osteoarthritis with more than 1.3 million patients in Europe. Today, there are several solutions to treat knee osteoarthritis depending on the degenerative nature of the disease : from conservative surgical treatment, such as High tibia Osteotomy (HTO), to prosthetic surgical treatment, such as Total Knee arthroplasty (TKA). The long-term success of these interventions is (1) the control of the lower limb alignment, during the intervention, which can be obtained by measuring the HKA angle between the hip, the knee and the ankle centers, and (2) the surgical planning allowing the preparation of the intervention, and for instance, the definition of the optimal cuts for the placement of a knee prosthesis based on the 3D model of the patient bone obtained from computerized tomography (CT) or MRI. We were interested, first, in the study and evaluation, in a clinical context, of the accuracy and precision of the methods used in computer-assisted orthopedic surgery for the localization of the hip center. We have thus proposed a new minimally invasive method especially adapted to HTO. We were interested, then, to the segmentation methods allowing the extraction of the knee bony surface from MRI for the surgical planning. We have also proposed a new automatic approach based on active shape models (ASM). Given the very encouraging results, the integration of our contributions in the clinical routine could, potentially, improve the medical benefits for the treatment of knee osteoarthritis
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Morra, Jonathan Harold. "Learning methods for brain MRI segmentation." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1905693471&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Krishnan, Nitya. "Multispectral segmentation of whole brain MRI." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3753.

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Thesis (M.S.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains vii, 89 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 56-59).
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Ezzadeen, Hani. "Extraction and segmentation of MRI brain images." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97949.

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Brain image segmentation is an active research in computer image analysis. The challenge lies in the fact that the brain anatomy is not identical for all normal subjects let alone subjects with abnormal tissue.
In this thesis, we explain the research we have implemented to extract the brain from T1-weighted MRI images, and then segment the brain into the three prominent compartments (i.e. the cerebellum and the two hemispheres of the cerebrum). The brain extraction is implemented using morphological operations after thresholding. The brain segmentation, however, is implemented in two separate steps. The first step segments the two hemispheres by approximating the midsagittal surface using mainly Radon transform. The second step segments the cerebellum using an atlas-based contour as an initial contour for the gradient vector flow active contour algorithm.
Validation tests have been performed for the brain extraction and cerebellum segmentation methods.
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Cederberg, Erik. "Adipose tissue segmentation in whole-body MRI." Thesis, Linköping University, Linköping University, Medical Informatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57465.

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Dreijer, Janto Frederick. "Cardiac MRI segmentation with conditional random fields." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85847.

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Thesis (PhD)-- Stellenbosch University, 2013.
ENGLISH ABSTRACT: This dissertation considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. The presence of papillary muscles near the endocardium border makes simple threshold based segmentation difficult. The endo- and epicardium are modelled as two series of radii which are inter-related using features describing shape and motion. Image features are derived from edge information from human annotated images. The features are combined within a Conditional Random Field (CRF) – a discriminatively trained probabilistic model. Loopy belief propagation is used to infer segmentations when an unsegmented video sequence is given. Powell’s method is applied to find CRF parameters by minimising the difference between ground truth annotations and the inferred contours. We also describe how the endocardium centre points are calculated from a single human-provided centre point in the first frame, through minimisation of frame alignment error. We present and analyse the results of segmentation. The algorithm exhibits robustness against inclusion of the papillary muscles by integrating shape and motion information. Possible future improvements are identified.
AFRIKAANSE OPSOMMING: Hierdie proefskrif bespreek die outomatiese segmentasie van die linkerhartkamer in kortas snit magnetiese resonansie beelde. Die teenwoordigheid van die papillêre spiere naby die endokardium grens maak eenvoudige drumpel gebaseerde segmentering moeilik. Die endo- en epikardium word gemodelleer as twee reekse van die radiusse wat beperk word deur eienskappe wat vorm en beweging beskryf. Beeld eienskappe word afgelei van die rand inligting van mens-geannoteerde beelde. Die funksies word gekombineer binne ’n CRF (Conditional Random Field) – ’n diskriminatief afgerigte waarskynlikheidsverdeling. “Loopy belief propagation” word gebruik om segmentasies af te lei wanneer ’n ongesegmenteerde video verskaf word. Powell se metode word toegepas om CRF parameters te vind deur die minimering van die verskil tussen mens geannoteerde segmentasies en die afgeleide kontoere. Ons beskryf ook hoe die endokardium se middelpunte bereken word vanaf ’n enkele mens-verskafte middelpunt in die eerste raam, deur die minimering van ’n raambelyningsfout. Ons analiseer die resultate van segmentering. Die algoritme vertoon robuustheid teen die insluiting van die papillêre spiere deur die integrasie van inligting oor die vorm en die beweging. Moontlike toekomstige verbeterings word geïdentifiseer.
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Murgasova, Maria. "Segmentation of brain MRI during early childhood." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/4354.

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The objective of this thesis is the development of automatic methods to measure the changes in volume and growth of brain structures in prematurely born infants. Automatic tools for accurate tissue quantification from magnetic resonance images can provide means for understanding how the neurodevelopmental effects of the premature birth, such as cognitive, neurological or behavioural impairment, are related to underlying changes in brain anatomy. Understanding these changes forms a basis for development of suitable treatments to improve the outcomes of premature birth. In this thesis we focus on the segmentation of brain structures from magnetic resonance images during early childhood. Most of the current brain segmentation techniques have been focused on the segmentation of adult or neonatal brains. As a result of rapid development, the brain anatomy during early childhood differs from anatomy of both adult and neonatal brains and therefore requires adaptations of available techniques to produce good results. To address the issue of anatomical differences of the brain during early childhood compared to other age-groups, population-specific deformable and probabilistic atlases are introduced. A method for generation of population-specific prior information in the form of a probabilistic atlas is proposed and used to enhance existing segmentation algorithms. The evaluation of registration-based and intensity-based approaches shows the techniques to be complementary in the quality of automatic segmentation in different parts of the brain. We propose a novel robust segmentation method combining the advantages of both approaches. The method is based on multiple label propagation using B-spline non-rigid registration followed by EM segmentation. Intensity in homogeneity is a shading artefact resulting from the acquisition process, which significantly affects modern high resolution MR data acquired at higher magnetic field strengths. A novel template based method focused on correcting the intensity inhomogeneity in data acquired at higher magnetic field strengths is therefore proposed. The proposed segmentation method combined with proposed intensity in homogeneity correction method offers a robust tool for quantification of volumes and growth of brain structures during early childhood. The tool has been applied to 67 T1-weigted images of subject at one and two years of age.
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Donoghue, Claire. "Analysis of MRI for knee osteoarthritis using machine learning." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24684.

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Approximately 8.5 million people in the UK (13.5% of the population) have osteoarthritis (OA) in one or both knees, with more than 6 million people in the UK suffering with painful osteoarthritis of the knee. In addition, an ageing population implies that an estimated 17 million people (twice as many as in 2012) are likely to be living with OA by 2030. Despite this, there exists no disease modifying drugs for OA and structural OA in MRI is poorly characterised. This motivates research to develop biomarkers and tools to aid osteoarthritis diagnosis from MRI of the knee. Previously many solutions for learning biomarkers have relied upon hand-crafted features to characterise and diagnose osteoarthritis from MRI. The methods proposed in this thesis are scalable and use machine learning to characterise large populations of the OAI dataset, with one experiment applying an algorithm to over 10,000 images. Studies of this size enable subtle characteristics of the dataset to be learnt and model many variations within a population. We present data-driven algorithms to learn features to predict OA from the appearance of the articular cartilage. An unsupervised manifold learning algorithm is used to compute a low dimensional representation of knee MR data which we propose as an imaging marker of OA. Previous metrics introduced for OA diagnosis are loosely based on the research communities intuition of the structural causes of OA progression, including morphological measures of the articular cartilage such as the thickness and volume. We demonstrate that there is a strong correlation between traditional morphological measures of the articular cartilage and the biomarkers identified using the manifold learning algorithm that we propose (R 2 = 0.75). The algorithm is extended to create biomarkers for different regions and sequences. A combination of these markers is proposed to yield a diagnostic imaging biomarker with superior performance. The diagnostic biomarkers presented are shown to improve upon hand-crafted morphological measure of disease status presented in the literature, a linear discriminant analysis (LDA) classification for early stage diagnosis of knee osteoarthritis results with an AUC of 0.9. From the biomarker discovery experiments we identified that intensity based affine registration of knee MRIs is not sufficiently robust for large scale image analysis, approximately 5% of these registrations fail. We have developed fast algorithms to compute robust affine transformations of knee MRI, which enables accurate pairwise registrations in large datasets. We model the population of images as a non-linear manifold, a registration is defined by the shortest geodesic path over the manifold representation. We identify sources of error in our manifold representation and propose fast mitigation strategies by checking for consistency across the manifold and by utilising multiple paths. These mitigation strategies are shown to improve registration accuracy and can be computed in less than 2 seconds with current architecture.
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Books on the topic "Knee segmentation in MRI"

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Bolog, Nicolae V., Gustav Andreisek, and Erika J. Ulbrich. MRI of the Knee. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08165-6.

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Advances in MRI of the knee for osteoarthritis. New Jersey: World Scientific, 2010.

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name, No. MRI atlas of orthopedics and traumatology of the knee. Berlin: Springer, 2003.

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Teller, Peter, Hermann König, Ulrich Weber, and Peter Hertel. MRI Atlas of Orthopedics and Traumatology of the Knee. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55620-3.

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F, Nielsen Poul M., Miller Karol, and SpringerLink (Online service), eds. Computational Biomechanics for Medicine: Soft Tissues and the Musculoskeletal System. New York, NY: Springer Science+Business Media, LLC, 2011.

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MRI of the knee. Gaithersburg, Md: Aspen Publishers, 1992.

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L, Munk Peter, and Helms Clyde A, eds. MRI of the knee. 2nd ed. Philadelphia: Lippincott-Raven, 1996.

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Lee, Christoph I. Incidental Meniscal Findings on Knee MRI. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0033.

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This chapter, found in the bone, joint, and extremity pain section of the book, provides a succinct synopsis of a key study examining the frequency of incidental findings on knee magnetic resonance imaging. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. Incidental meniscal damage on MRI was shown to be common in the general population, especially among the elderly, and is not necessarily attributable to patients’ knee symptoms. Authors advise those interpreting MRI reports and planning interventions that there is a high prevalence of incidental tears even among those without knee symptoms. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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Fenstermacher, Marc. MRI of the Knee CD-ROM (Body MRI Series on CD-ROM). A Hodder Arnold Publication, 1997.

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Mri of the Knee (Clinical Diagnostic Imaging Series). Aspen Publishers, 1991.

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Book chapters on the topic "Knee segmentation in MRI"

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Thengade, Anita, and A. M. Rajurkar. "Segmentation of Knee Bone Using MRI." In Applied Computer Vision and Image Processing, 237–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4029-5_24.

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Wang, Zehan, Claire Donoghue, and Daniel Rueckert. "Patch-Based Segmentation without Registration: Application to Knee MRI." In Machine Learning in Medical Imaging, 98–105. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02267-3_13.

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Folkesson, Jenny, Ole Fogh Olsen, Paola Pettersen, Erik Dam, and Claus Christiansen. "Combining Binary Classifiers for Automatic Cartilage Segmentation in Knee MRI." In Computer Vision for Biomedical Image Applications, 230–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11569541_24.

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Lorigo, Liana M., Olivier Faugeras, W. E. L. Grimson, Renaud Keriven, and Ron Kikinis. "Segmentation of bone in clinical knee MRI using texture-based geodesic active contours." In Medical Image Computing and Computer-Assisted Intervention — MICCAI’98, 1195–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0056309.

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Folkesson, Jenny, Erik Dam, Ole Fogh Olsen, Paola Pettersen, and Claus Christiansen. "Automatic Segmentation of the Articular Cartilage in Knee MRI Using a Hierarchical Multi-class Classification Scheme." In Lecture Notes in Computer Science, 327–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566465_41.

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M. S., Mallikarjunaswamy, Mallikarjun S. Holi, Rajesh Raman, and J. S. Sujana Theja. "Accurate Techniques of Thickness and Volume Measurement of Cartilage from Knee Joint MRI Using Semiautomatic Segmentation Methods." In New Trends in Computational Vision and Bio-inspired Computing, 1017–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41862-5_103.

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Kashyap, Satyananda, Ipek Oguz, Honghai Zhang, and Milan Sonka. "Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 344–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46723-8_40.

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Hegazi, Tarek M., and Jim S. Wu. "Knee." In Musculoskeletal MRI, 109–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26777-3_5.

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Mohiaddin, Raad H., and Donald B. Longmore. "Knee." In MRI Atlas of Normal Anatomy, 155–81. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2990-9_9.

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Pereira, Hélder, Sérgio Gomes, José Carlos Vasconcelos, Laura Soares, Rogério Pereira, Joaquim Miguel Oliveira, Rui L. Reis, and Joao Espregueira-Mendes. "MRI Laxity Assessment." In Rotatory Knee Instability, 49–61. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32070-0_5.

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Conference papers on the topic "Knee segmentation in MRI"

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Almajalid, Rania, Juan Shan, Maolin Zhang, Garrett Stonis, and Ming Zhang. "Knee Bone Segmentation on Three-Dimensional MRI." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00280.

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S.A., Revathi, and Ganga Holi. "Cartilage Segmentation of Knee OsteoArthritis From Magnetic Resonance Images(MRI)." In 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC). IEEE, 2018. http://dx.doi.org/10.1109/icaecc.2018.8479529.

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Lim, Mikhiel, and Ilker Hacihaliloglu. "Segmentation of knee MRI using structure enhanced local phase filtering." In SPIE Medical Imaging, edited by Georgia D. Tourassi and Samuel G. Armato. SPIE, 2016. http://dx.doi.org/10.1117/12.2216568.

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Dai, Wei, Boyeong Woo, Siyu Liu, Matthew Marques, Fangfang Tang, Stuart Crozier, Craig Engstrom, and Shekhar Chandra. "Can3d: Fast 3d Knee Mri Segmentation Via Compact Context Aggregation." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433784.

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Zhou, L., R. Chav, T. Cresson, G. Chartrand, and J. de Guise. "3D knee segmentation based on three MRI sequences from different planes." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7590881.

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Prasoon, Adhish, Christian Igel, Marco Loog, Francois Lauze, Erik B. Dam, and Mads Nielsen. "Femoral cartilage segmentation in Knee MRI scans using two stage voxel classification." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610787.

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Panfilov, Egor, Aleksei Tiulpin, Stefan Klein, Miika T. Nieminen, and Simo Saarakkala. "Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00057.

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Lee, Hansang, Helen Hong, and Junmo Kim. "BCD-NET: A novel method for cartilage segmentation of knee MRI via deep segmentation networks with bone-cartilage-complex modeling." In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018. http://dx.doi.org/10.1109/isbi.2018.8363866.

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Williams, Tomos G., Graham Vincent, Mike Bowes, Tim Cootes, Sharon Balamoody, Charles Hutchinson, John C. Waterton, and Chris J. Taylor. "Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee MRI." In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010. http://dx.doi.org/10.1109/isbi.2010.5490316.

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Lynch, John A., Souhil Zaim, Jenny Zhao, Alexander Stork, Charles G. Peterfy, and Harry K. Genant. "Cartilage segmentation of 3D MRI scans of the osteoarthritic knee combining user knowledge and active contours." In Medical Imaging 2000, edited by Kenneth M. Hanson. SPIE, 2000. http://dx.doi.org/10.1117/12.387758.

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Reports on the topic "Knee segmentation in MRI"

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Anantharajan, Shenbagarajan, Shenbagalakshmi Gunasekaran, and Elamparithi Pandian. MRI Brain Tumour Segmentation Based on Fish Chaining Transition Optimization Algorithm. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2020. http://dx.doi.org/10.7546/crabs.2020.02.14.

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