High resolution volume quantification of the knee joint space based on a semi-automatic segmentation of computed tomography images

Mezlini, Houda; Youssef, Rabaa; Bouhadoun, Hamid; Budyn, Elisa; Laredo, J. D.; Ghalila, Sylvie Sevestre; Chappard, Christine

doi:10.1109/iwssip.2015.7314201

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…29 In a preliminary study, we have found that the segmentation method continues to have good performance on lower resolution images with a cone beam geometry acquisition. 28 At the present time, new conebeam CT machines are already available on the market for musculoskeletal-extremity imaging. The scans are performed in a weight-bearing position with fast acquisition 36 and provide good image quality for bone and an adequate quality for soft tissue.…”

Section: Discussionmentioning

confidence: 99%

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Three‐dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

Mezlini-Gharsallah

Youssef

et al. 2018

Journal Orthopaedic Research

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One of the most important characteristic of knee osteoarthritis (OA) is the joint space (JS) width narrowing. Measurements are usually performed on two dimensional (2D) X‐rays. We propose and validate a new method to assess the 3D joint space at the medial knee compartment using high resolution peripheral computed tomography images. A semi‐automated method was developed to obtain a distance 3D map between femur an tibia with the following parameters: volume, minimum, maximum, mean, standard deviation, median, asymmetry, and entropy. We analyzed 71 knee specimens (mean age: 85 years), radiographs were performed for the Kellgren Lawrence (KL) score grading. In a subgroup of 41 specimens, the histopathological Outerbridge and meniscal classifications were performed and then cores were harvested from the tibial plateau in three different positions (posterior, central, and peripheral) and imaged at 10 µm of resolution to measure the cartilage thickness. Minimum, maximum, mean, and median were statistically lower and entropy higher between knee specimens classified as KL = 0 and KL = 3–4. Gr1 and 2 were statistically different from Gr3‐4 for minimum, asymmetry, entropy using the Outerbridge classification and Gr1 was statistically different from Gr3–4 using the meniscal classification. Asymmetry, minimum, mean, median and entropy were significantly correlated with cartilage thickness. Parameters extracted from a 3D map of the medial joint space indicate local variations of JS and are related to local measurements of tibial cartilage thickness, and could be consequently useful to identify early OA. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 36:2380–2391, 2018.

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Section: Discussionmentioning

confidence: 99%

“…27 Cartilage thickness was measured by the sphere method after manual contouring of the cartilage. 28…”

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confidence: 99%

Three‐dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

Mezlini-Gharsallah

Youssef

et al. 2018

Journal Orthopaedic Research

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“…A segmentation method for the measurement of local 3D variables in the knees articular space, on CT images with high peripheral resolution, is proposed by Mezlini et al [73]. The approach can be divided in a sequence of steps: the first step consists on the manual knee image reorientation due to the variety of orientations present in the images.…”

Section: Active Contoursmentioning

confidence: 99%

3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review

2018

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This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.

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“…Computed tomography has been widely used to diagnose knee joint pathologies. Correspondingly, knee CT images have been automatically or semiautomatically processed and analyzed (e.g., [1][2][3][4][5]) for computer-aided diagnosis. Subburaj et al [1] proposed a computer graphicsbased method to automatically localize and label anatomical landmarks on the 3D bone model reconstructed from knee CT images of a patient.…”

Section: Introductionmentioning

confidence: 99%

“…Wu et al [4] proposed to segment multiple bones around the knee joint with severe pathologies to help patient-specific orthopedic knee surgery planning. Mezlini et al [5] proposed to measure the knee joint space based on semiautomatic CT image segmentation for the monitoring of osteoarthritis progression. However, to the best of our knowledge, no efforts have been published specifically for automatic measurement on knee CT images for the purpose of patellar dislocation diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis

Sun

Kong

Huang

et al. 2020

Computational and Mathematical Methods in Medicine

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Traditionally, for diagnosing patellar dislocation, clinicians make manual geometric measurements on computerized tomography (CT) images taken in the knee area, which is often complex and error-prone. erefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly segment the patella and the femur regions on the CT images and then measure two geometric quantities, patellar tilt angle (PTA), and patellar lateral shift (PLS) automatically on the segmentation results, which are finally used to assist in diagnoses. e proposed quantities are proved valid and the proposed algorithms are proved effective by experiments.

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High resolution volume quantification of the knee joint space based on a semi-automatic segmentation of computed tomography images

Cited by 4 publications

References 13 publications

Three‐dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

Three‐dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review

Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis

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