Automatic Human Knee Cartilage Segmentation From 3-D Magnetic Resonance Images

被引:88
|
作者
Dodin, Pierre [2 ]
Pelletier, Jean-Pierre [1 ]
Martel-Pelletier, Johanne [1 ]
Abram, Francois [2 ]
机构
[1] Univ Montreal Hosp Res Ctr CRCHUM, Notre Dame Hosp, Osteoarthritis Res Unit, Montreal, PQ H2L 4M1, Canada
[2] ArthroVis Inc, Montreal, PQ H2K 1B6, Canada
关键词
Cartilage volume; image resampling; magnetic resonance imaging (MRI); texture analysis; surface parameterization; three-dimensional segmentation; ARTICULAR-CARTILAGE; OSTEOARTHRITIC KNEES; MR; VOLUME; QUANTIFICATION; SURFACES; PRECISION; THICKNESS; JOINT; ECHO;
D O I
10.1109/TBME.2010.2058112
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This study aimed at developing a new automatic segmentation algorithm for human knee cartilage volume quantification from MRI. Imaging was performed using a 3T scanner and a knee coil, and the exam consisted of a double echo steady state (DESS) sequence, which contrasts cartilage and soft tissues including the synovial fluid. The algorithm was developed on MRI 3-D images in which the bone-cartilage interface for the femur and tibia was segmented by an independent segmentation process, giving a parametric surface of the interface. First, the MR images are resampled in the neighborhood of the bone surface. Second, by using texture-analysis techniques optimized by filtering, the cartilage is discriminated as a bright and homogeneous tissue. This process of excluding soft tissues enables the detection of the external boundary of the cartilage. Third, a technology based on a Bayesian decision criterion enables the automatic separation of the cartilage and synovial fluid. Finally, the cartilage volume and changes in volume for an individual between visits was assessed using the developed technology. Validation included first, for nine knee osteoarthritis patients, a comparison of the cartilage volume and changes over time between the developed automatic system and a validated semi-automatic cartilage volume system, and second, for five knee osteoarthritis patients, a test-retest procedure. Data revealed excellent Pearson correlations and Dice similarity coefficients (DSC) for the global knee (r = 0.96, p < 0.0001, and median DSC = 0.84), for the femur (r = 0.95, p < 0.0001, and median DSC = 0.85), and the tibia (r = 0.83, p < 0.0001, and median DSC = 0.84). Very good similarity between the automatic and semi-automatic methods in regard to cartilage loss was also found for the global knee (r = 0.76 and p = 0.016) as well as for the femur (r = 0.79 and p = 0.011). The test-retest revealed an excellent measurement error of -0.3 +/- 1.6% for the global knee and 0.14 +/- 1.7% for the femur. In conclusion, the newly developed fully automatic method described herein provides accurate and precise quantification of knee cartilage volume and will be a valuable tool for clinical follow-up studies.
引用
收藏
页码:2699 / 2711
页数:13
相关论文
共 50 条
  • [21] Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images
    Liew, AWC
    Yan, H
    CURRENT MEDICAL IMAGING, 2006, 2 (01) : 91 - 103
  • [22] Automatic 3-D Segmentation of Endocardial Border of the Left Ventricle From Ultrasound Images
    Santiago, Carlos
    Nascimento, Jacinto C.
    Marques, Jorge S.
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2015, 19 (01) : 339 - 348
  • [23] Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images
    Jianfei Pang
    PengYue Li
    Mingguo Qiu
    Wei Chen
    Liang Qiao
    Journal of Digital Imaging, 2015, 28 : 695 - 703
  • [24] AUTOMATIC MULTI-ATLAS-BASED CARTILAGE SEGMENTATION FROM KNEE MR IMAGES
    Shan, Liang
    Charles, Cecil
    Niethammer, Marc
    2012 9TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2012, : 1028 - 1031
  • [25] Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images
    Pang, Jianfei
    Li, PengYue
    Qiu, Mingguo
    Chen, Wei
    Qiao, Liang
    JOURNAL OF DIGITAL IMAGING, 2015, 28 (06) : 695 - 703
  • [26] Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images
    Yang, Zhengyi
    Fripp, Jurgen
    Chandra, Shekhar S.
    Neubert, Ales
    Xia, Ying
    Strudwick, Mark
    Paproki, Anthony
    Engstrom, Craig
    Crozier, Stuart
    PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (04): : 1441 - 1459
  • [27] Cartilage morphometry and magnetic susceptibility measurement for knee osteoarthritis with automatic cartilage segmentation
    Zhang, Qi
    Geng, Jiaolun
    Zhang, Ming
    Kan, Tianyou
    Wang, Liao
    Ai, Songtao
    Wei, Hongjiang
    Zhang, Lichi
    Liu, Chenglei
    QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2023, 13 (06) : 3508 - +
  • [28] FULLY AUTOMATIC PLAQUE SEGMENTATION IN 3-D CAROTID ULTRASOUND IMAGES
    Cheng, Jieyu
    Li, He
    Xiao, Feng
    Fenster, Aaron
    Zhang, Xuming
    He, Xiaoling
    Li, Ling
    Ding, Mingyue
    ULTRASOUND IN MEDICINE AND BIOLOGY, 2013, 39 (12): : 2431 - 2446
  • [29] 3-D Spot Modeling for Automatic Segmentation of cDNA Microarray Images
    Zacharia, Eleni
    Maroulis, Dimitris
    IEEE TRANSACTIONS ON NANOBIOSCIENCE, 2010, 9 (03) : 181 - 192
  • [30] Automatic Knee Cartilage Segmentation Using Multi-Feature Support Vector Machine and Elastic Region Growing for Magnetic Resonance Images
    Wang, Pin
    He, Xuan
    Li, Yongming
    Zhu, Xueru
    Chen, Wei
    Qiu, Mingguo
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2016, 6 (04) : 948 - 956