A reproducibility study of knee cartilage volume and thickness values derived by fully automatic segmentation based on three-dimensional dual-echo in steady state data from 1.5 T and 3 T magnetic resonance imaging

被引:0
作者
Zhang, Ranxu [1 ]
Zhou, Xiaoyue [2 ]
Raithel, Esther [3 ]
Ren, Congcong [1 ]
Zhang, Ping [1 ]
Li, Junfei [1 ]
Bai, Lin [1 ]
Zhao, Jian [1 ]
机构
[1] Third Hosp Hebei Med Univ, Dept CT MR, Hebei Prov Biomech Key Lab Orthoped, Shijiazhuang 050051, Peoples R China
[2] Siemens Healthineers Ltd, MR Collaborat, Shanghai 200126, Peoples R China
[3] Siemens Healthcare GmbH, Erlangen, Germany
关键词
Cartilage segmentation; Fully automatic segmentation; 3D DESS; Cartilage quantification; ARTICULAR-CARTILAGE; MRI; OSTEOARTHRITIS;
D O I
10.1007/s10334-023-01122-x
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objective To evaluate the repeatability of cartilage volume and thickness values at 1.5 T MRI using a fully automatic cartilage segmentation method and reproducibility of the method between 1.5 T and 3 T data.Methods The study included 20 knee joints from 10 healthy subjects with each subject having undergone double-knee MRI. All knees were scanned at 1.5 T and 3 T MR scanners using a three-dimensional (3D) high-resolution dual-echo in steady state (DESS) sequence. Cartilage volume and thickness of 21 subregions were quantified using a fully automatic cartilage segmentation research application (MR Chondral Health, version 3.0, Siemens Healthcare, Erlangen, Germany). The volume and thickness values derived from fully automatically computed segmentation masks were analyzed for the scan-rescan data from the same volunteers. The accuracy of the automatic segmentation of the cartilage in 1.5 T images was evaluated by the dice similarity coefficient (DSC) and Hausdorff distance (HD) using the manually corrected segmentation as a reference. The volume and thickness values calculated from 1.5 T and 3 T were also compared.Results No statistically significant differences were found for cartilage thickness or volume across all subregions between the scan-rescanned data at 1.5 T (P > 0.05). The mean DSC between the fully automatic and manually corrected knee cartilage segmentation contours at 1.5 T was 0.9946. The average value of HD was 2.41 mm. Overall, there was no statistically significant difference in the cartilage volume or thickness in most-subregions between the two field strengths (P > 0.05) except for the medial region of femur and tibia. Bland-Altman plot and intraclass correlation coefficient (ICC) showed high consistency between results obtained based on same and different scanning sequences.Conclusion The cartilage segmentation software had high repeatability for DESS images obtained from the same device. In addition, the overall reproducibility of the images obtained from equipment of two different field strengths was satisfactory.
引用
收藏
页码:69 / 82
页数:14
相关论文
共 38 条
  • [1] Automated tibiofemoral joint segmentation based on deeply supervised 2D-3D ensemble U-Net: Data from the Osteoarthritis Initiative
    Abd Latif, Muhamad Hafiz
    Faye, Ibrahima
    [J]. ARTIFICIAL INTELLIGENCE IN MEDICINE, 2021, 122
  • [2] Focused shape models for hip joint segmentation in 3D magnetic resonance images
    Chandra, Shekhar S.
    Xia, Ying
    Engstrom, Craig
    Crozier, Stuart
    Schwarz, Raphael
    Fripp, Jurgen
    [J]. MEDICAL IMAGE ANALYSIS, 2014, 18 (03) : 567 - 578
  • [3] Knee cartilage topography, thickness, and contact areas from MRI: in-vitro calibration and in-vivo measurements
    Cohen, ZA
    McCarthy, DM
    Kwak, SD
    Legrand, P
    Fogarasi, F
    Ciaccio, EJ
    Ateshian, GA
    [J]. OSTEOARTHRITIS AND CARTILAGE, 1999, 7 (01) : 95 - 109
  • [4] CAN3D: Fast 3D medical image segmentation via compact context aggregation
    Dai, Wei
    Woo, Boyeong
    Liu, Siyu
    Marques, Matthew
    Engstrom, Craig
    Greer, Peter B.
    Crozier, Stuart
    Dowling, Jason A.
    Chandra, Shekhar S.
    [J]. MEDICAL IMAGE ANALYSIS, 2022, 82
  • [5] The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
    Desai, Arjun D.
    Caliva, Francesco
    Iriondo, Claudia
    Mortazi, Aliasghar
    Jambawalikar, Sachin
    Bagci, Ulas
    Perslev, Mathias
    Igel, Christian
    Dam, Erik B.
    Gaj, Sibaji
    Yang, Mingrui
    Li, Xiaojuan
    Deniz, Cem M.
    Juras, Vladimir
    Regatte, Ravinder
    Gold, Garry E.
    Hargreaves, Brian A.
    Pedoia, Valentina
    Chaudhari, Akshay S.
    [J]. RADIOLOGY-ARTIFICIAL INTELLIGENCE, 2021, 3 (03)
  • [6] A review on segmentation of knee articular cartilage: from conventional methods towards deep learning
    Ebrahimkhani, Somayeh
    Jaward, Mohamed Hisham
    Cicuttini, Flavia M.
    Dharmaratne, Anuja
    Wang, Yuanyuan
    de Herrera, Alba G. Seco
    [J]. ARTIFICIAL INTELLIGENCE IN MEDICINE, 2020, 106
  • [7] Eckstein F, 1998, CLIN ORTHOP RELAT R, P137
  • [8] Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment
    Eckstein, F.
    Cicuttini, Flavia M.
    Raynauld, J. -P.
    Waterton, J. C.
    Peterfy, C.
    [J]. OSTEOARTHRITIS AND CARTILAGE, 2006, 14 : A46 - A75
  • [9] Double echo steady state magnetic resonance imaging of knee articular cartilage at 3 Tesla: a pilot study for the Osteoarthritis Initiative
    Eckstein, F
    Hudelmaier, M
    Wirth, W
    Kiefer, B
    Jackson, R
    Yu, J
    Eaton, CB
    Schneider, E
    [J]. ANNALS OF THE RHEUMATIC DISEASES, 2006, 65 (04) : 433 - 441
  • [10] High-resolution cartilage imaging of the knee at 3 T: Basic evaluation of modern isotropic 3D MR-sequences
    Friedrich, Klaus M.
    Reiter, Gert
    Kaiser, Bernd
    Mayerhoefer, Marius
    Deimling, Michael
    Jellus, Vladimir
    Horger, Wilhelm
    Trattnig, Siegfried
    Schweitzer, Mark
    Salomonowitz, Erich
    [J]. EUROPEAN JOURNAL OF RADIOLOGY, 2011, 78 (03) : 398 - 405