A novel algorithm for computer-assisted measurement of cervical length from transvaginal ultrasound images.
The cervical length measured by transvaginal ultrasound is a proven clinical tool for predicting premature birth. The standard manual measurement of the cervix is limited by variability in the technique. In this research, we develop the first computer algorithm that is able to identify the anatomic landmarks of the cervix on a transvaginal ultrasound image and determine the standard cervical length. The system is composed of four stages: The first stage is adaptive speckle suppression using variable length sticks algorithm. The second stage is the location of the internal cervical opening or "os" using a region-based segmentation. The third stage is delineation of the cervical canal. The fourth stage uses gray level summation patterns and prior knowledge to first localize the tissue boundary of the external cervix, and then use a template to determine the specific location of the external os. The cervical length is determined and calculated to image scale. To validate the proposed algorithm, 101 cervical ultrasound images were selected from a series of 37 examinations performed on 17 patients over an eight-month period. Repeated measurements of cervical length using the computer-assisted method were compared with those carried out by two experienced sonographers. The median intraobserver variability for the 101 images using the computer-assisted method was significantly smaller than that of the manual method by either sonographer. In a pairwise comparison, the mean cervical length for the computer method matches with the mean manual cervical length.
Department of Electrical and Computer Engineering, University of Missouri-Columbia, Columbia, MO 65211, USA. mw463@mizzou.edu
Min
M
Wu
Robert F
RF
Fraser
Chang Wen
CW
Chen
D000465:Algorithms; D001185:Artificial Intelligence; D002584:Cervix Uteri / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated; D011247:Pregnancy; D047928:Premature Birth / Q000000981:diagnostic imaging; D018570:Risk Assessment / Q000379:methods; D016216:Ultrasonography, Prenatal / Q000379:methods*; D014621:Vagina / Q000000981:diagnostic imaging