Automatic segmentation and motion analysis of the knee joint based on MRI and 4DCT images

H. Chen

    Research output: ThesisPhD Thesis - Research UT, graduation UT

    18 Downloads (Pure)

    Abstract

    Lower limbs disorders, such as osteoarthritis (OA), knee bursitis, meniscal lesions, are musculoskeletal diseases that affect the hips, knees and legs, and usually lead to chronic pain conditions and limited mobility. Knee joint pains can interfere with daily activities, from sports to climbing stairs to walking. In this sense, a relatively minor knee defect can have a great impact on everyday life and create negative conditions for further health deterioration. In the past decades, rapid advances in medical imaging and increased understanding of musculoskeletal mechanics, and as a result diagnosis and effective treatment of knee joint disorders have improved substantially. Nevertheless, the available tools for researchers, radiologists and orthopaedic surgeons to assess the biomechanical condition of patients still lack in delivering a quantitative analysis of muscle and ligament ruptures, cartilage deterioration and patella misalignment, forcing the clinicians to rely mostly on their own subjective judgement which can lead sub-optimal care. One of the main bottlenecks in delivering quantitative digital models of the knee joint is the fast and accurate segmentation of the various knee structures from medical images. Manual segmentation is intrinsically subjective and time-consuming despite the developments in software, and accurate segmentation requires both clinical and user interface experience. As a part of the ERC BioMechTools project, the work described in this thesis is dedicated to advancing existing methods and developing new methods for robust, accurate and maximally automated segmentation and clinical measure determination relevant to pathology assessment in the knee joint.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • University of Twente
    Supervisors/Advisors
    • Verdonschot, Nicolaas Jacobus Joseph, Supervisor
    • Kang, Y., Supervisor
    • Sprengers, A.M.J., Co-Supervisor
    Award date6 Feb 2020
    Place of PublicationEnschede
    Publisher
    Print ISBNs978-90-365-4947-9
    DOIs
    Publication statusPublished - 6 Feb 2020

    Fingerprint

    Knee Joint
    Knee
    Musculoskeletal Diseases
    Bursitis
    Patella
    Knee Osteoarthritis
    Arthralgia
    Diagnostic Imaging
    Mechanics
    Ligaments
    Chronic Pain
    Walking
    Cartilage
    Sports
    Hip
    Rupture
    Lower Extremity
    Leg
    Software
    Research Personnel

    Cite this

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    title = "Automatic segmentation and motion analysis of the knee joint based on MRI and 4DCT images",
    abstract = "Lower limbs disorders, such as osteoarthritis (OA), knee bursitis, meniscal lesions, are musculoskeletal diseases that affect the hips, knees and legs, and usually lead to chronic pain conditions and limited mobility. Knee joint pains can interfere with daily activities, from sports to climbing stairs to walking. In this sense, a relatively minor knee defect can have a great impact on everyday life and create negative conditions for further health deterioration. In the past decades, rapid advances in medical imaging and increased understanding of musculoskeletal mechanics, and as a result diagnosis and effective treatment of knee joint disorders have improved substantially. Nevertheless, the available tools for researchers, radiologists and orthopaedic surgeons to assess the biomechanical condition of patients still lack in delivering a quantitative analysis of muscle and ligament ruptures, cartilage deterioration and patella misalignment, forcing the clinicians to rely mostly on their own subjective judgement which can lead sub-optimal care. One of the main bottlenecks in delivering quantitative digital models of the knee joint is the fast and accurate segmentation of the various knee structures from medical images. Manual segmentation is intrinsically subjective and time-consuming despite the developments in software, and accurate segmentation requires both clinical and user interface experience. As a part of the ERC BioMechTools project, the work described in this thesis is dedicated to advancing existing methods and developing new methods for robust, accurate and maximally automated segmentation and clinical measure determination relevant to pathology assessment in the knee joint.",
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    Automatic segmentation and motion analysis of the knee joint based on MRI and 4DCT images. / Chen, H.

    Enschede : University of Twente, 2020. 143 p.

    Research output: ThesisPhD Thesis - Research UT, graduation UT

    TY - THES

    T1 - Automatic segmentation and motion analysis of the knee joint based on MRI and 4DCT images

    AU - Chen, H.

    PY - 2020/2/6

    Y1 - 2020/2/6

    N2 - Lower limbs disorders, such as osteoarthritis (OA), knee bursitis, meniscal lesions, are musculoskeletal diseases that affect the hips, knees and legs, and usually lead to chronic pain conditions and limited mobility. Knee joint pains can interfere with daily activities, from sports to climbing stairs to walking. In this sense, a relatively minor knee defect can have a great impact on everyday life and create negative conditions for further health deterioration. In the past decades, rapid advances in medical imaging and increased understanding of musculoskeletal mechanics, and as a result diagnosis and effective treatment of knee joint disorders have improved substantially. Nevertheless, the available tools for researchers, radiologists and orthopaedic surgeons to assess the biomechanical condition of patients still lack in delivering a quantitative analysis of muscle and ligament ruptures, cartilage deterioration and patella misalignment, forcing the clinicians to rely mostly on their own subjective judgement which can lead sub-optimal care. One of the main bottlenecks in delivering quantitative digital models of the knee joint is the fast and accurate segmentation of the various knee structures from medical images. Manual segmentation is intrinsically subjective and time-consuming despite the developments in software, and accurate segmentation requires both clinical and user interface experience. As a part of the ERC BioMechTools project, the work described in this thesis is dedicated to advancing existing methods and developing new methods for robust, accurate and maximally automated segmentation and clinical measure determination relevant to pathology assessment in the knee joint.

    AB - Lower limbs disorders, such as osteoarthritis (OA), knee bursitis, meniscal lesions, are musculoskeletal diseases that affect the hips, knees and legs, and usually lead to chronic pain conditions and limited mobility. Knee joint pains can interfere with daily activities, from sports to climbing stairs to walking. In this sense, a relatively minor knee defect can have a great impact on everyday life and create negative conditions for further health deterioration. In the past decades, rapid advances in medical imaging and increased understanding of musculoskeletal mechanics, and as a result diagnosis and effective treatment of knee joint disorders have improved substantially. Nevertheless, the available tools for researchers, radiologists and orthopaedic surgeons to assess the biomechanical condition of patients still lack in delivering a quantitative analysis of muscle and ligament ruptures, cartilage deterioration and patella misalignment, forcing the clinicians to rely mostly on their own subjective judgement which can lead sub-optimal care. One of the main bottlenecks in delivering quantitative digital models of the knee joint is the fast and accurate segmentation of the various knee structures from medical images. Manual segmentation is intrinsically subjective and time-consuming despite the developments in software, and accurate segmentation requires both clinical and user interface experience. As a part of the ERC BioMechTools project, the work described in this thesis is dedicated to advancing existing methods and developing new methods for robust, accurate and maximally automated segmentation and clinical measure determination relevant to pathology assessment in the knee joint.

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    ER -