Improved detection of synovial boundaries in ultrasound examination by using a cascade of active-contours.

Rheumatoid arthritis (RA) is a chronic multisystemic autoimmune disease, with an unclear etiopathogenesis. Its early diagnosis and activity assessment are essential to adjust the proper therapy. Among the different imaging techniques, ultrasonography (US) allows direct visualization of early inflammatory joint changes as synovitis, being also rapidly performed and easily accepted by patients. We propose an algorithm to semi-automatically detect synovial boundaries on US images, requiring minimal user interaction. In order to identify the synovia-bone and the synovia-soft tissues interfaces, and to tackle the morphological variability of diseased joints, a cascade of two different active contours is developed, whose composition corresponds to the whole synovial boundary. The algorithm was tested on US images acquired from proximal interphalangeal (PIP) and metacarpophalangeal (MCP) finger joints of 34 subjects. The results have been compared with a consensus manual segmentation. We obtained an overall mean sensitivity of 85±13%, and a mean Dice's similarity index of 80±8%, with a mean Hausdorff distance from the manual segmentation of 28±10 pixels (approximately 1.4±0.5mm), that are a better performance than those obtained by the raters with respect to the consensus.

[1]  Henk Visser,et al.  Early diagnosis of rheumatoid arthritis. , 2005, Best practice & research. Clinical rheumatology.

[2]  F. Arnett.  Revised criteria for the classification of rheumatoid arthritis. , 1990, Orthopedic nursing.

[3]  D. Felson,et al.  Imaging of synovitis in osteoarthritis: current status and outlook. , 2011, Seminars in arthritis and rheumatism.

[4]  Stanley Osher,et al.  Total variation based image restoration with free local constraints , 1994, Proceedings of 1st International Conference on Image Processing.

[5]  Bing Li,et al.  Active Contour External Force Using Vector Field Convolution for Image Segmentation , 2007, IEEE Transactions on Image Processing.

[6]  L. Bonomo,et al.  Usefulness of Ultrasound Imaging in Detecting Psoriatic Arthritis of Fingers and Toes in Patients with Psoriasis , 2011, Clinical & developmental immunology.

[7]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.

[8]  M. Liang,et al.  The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. , 1988, Arthritis and rheumatism.

[9]  Bo Wang,et al.  A Novel Edge Enhancement Method for Ultrasound Imaging , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[10]  Demetri Terzopoulos,et al.  Deformable models , 2000, The Visual Computer.

[11]  Andrew F. Laine,et al.  Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing , 1998, IEEE Transactions on Medical Imaging.

[12]  M. Nikolova.  An Algorithm for Total Variation Minimization and Applications , 2004 .

[13]  K. Terasawa,et al.  Insights to Clinical Use of Serial Determination in Titers of Cyclic Citrullinated Peptide Autoantibodies , 2007, Mediators of inflammation.

[14]  A. Koch,et al.  Angiogenesis: Implications for rheumatoid arthritis , 1998 .

[15]  W. Grassi,et al.  Ultrasonography in rheumatology: an evolving technique , 1998, Annals of the rheumatic diseases.

[16]  G. Firestein,et al.  Starving the synovium: angiogenesis and inflammation in rheumatoid arthritis. , 1999, The Journal of clinical investigation.

[17]  Alin Achim,et al.  Novel Bayesian multiscale method for speckle removal in medical ultrasound images , 2001, IEEE Transactions on Medical Imaging.

[18]  James S. Duncan,et al.  Deformable boundary finding in medical images by integrating gradient and region information , 1996, IEEE Trans. Medical Imaging.

[19]  Norbert Wiener,et al.  Extrapolation, Interpolation, and Smoothing of Stationary Time Series , 1964 .

[20]  A. Tannenbaum,et al.  Despeckling of medical ultrasound images , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[21]  Franz Kainberger,et al.  Musculoskeletal ultrasound including definitions for ultrasonographic pathology. , 2005, The Journal of rheumatology.

[22]  J M Thijssen,et al.  Improvement of lesion detectability by speckle reduction filtering: a quantitative study. , 1993, Ultrasonic imaging.

[23]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[24]  A. Koch,et al.  Angiogenesis and chemokines in rheumatoid arthritis and other systemic inflammatory rheumatic diseases , 2002, Journal of cellular and molecular medicine.

[25]  Peter Nightingale,et al.  Predictive value of antibodies to cyclic citrullinated peptide in patients with very early inflammatory arthritis. , 2005, The Journal of rheumatology.

[26]  R. Chang,et al.  The American College of Rheumatology 1991 revised criteria for the classification of global functional status in rheumatoid arthritis. , 1992, Arthritis and rheumatism.

[27]  A. Zwinderman,et al.  Radiographic damage of large joints in long-term rheumatoid arthritis and its relation to function. , 2000, Rheumatology.

[28]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Akshay K. Singh,et al.  Deformable models in medical image analysis , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[30]  Klaus Bohndorf,et al.  Contrast enhanced gray-scale sonography in assessment of joint vascularity in rheumatoid arthritis: results from the IACUS study group , 2005, European Radiology.

[31]  J. Braun,et al.  Prevalence of spondylarthropathies in HLA-B27 positive and negative blood donors. , 1998, Arthritis and rheumatism.

[32]  G S Alarcón,et al.  Epidemiology of rheumatoid arthritis. , 1995, Rheumatic diseases clinics of North America.