A Novel Algorithm for Computer-Assisted Measurement of Cervical Length from Transvaginal Ultrasound Images
Abstract
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.
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Wu et al. proposed a tool for preterm labor prediction by using computer-assisted measurement of the cervix on transvaginal US images to overcome the issues of poor reproducibility and sonographer dependency on manual cervical length measurements.
“The early study of Wu et al. proposed a tool for preterm labor prediction by using computer-assisted measurement of the cervix on transvaginal US images to overcome the issues of poor reproducibility and sonographer dependency on manual cervical length measurements [166].”
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Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
“The early study of Wu et al. proposed a tool for preterm labor prediction by using computer-assisted measurement of the cervix on transvaginal US images to overcome the issues of poor reproducibility and sonographer dependency on manual cervical length measurements [166].”
Method
- Authors
- Wu, M., Fraser, R. F., & Chen, C. W.
- Affiliations
- University of Missouri (US)
- St. John's Hospital (US)
- Florida Institute of Technology (US)
- ORCID
- Wu, M.
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2004-09
- Volume
- 8
- Issue
- 3
- Pages
- 333-342
- ISSN
- 1089-7771 (ISSN-L)
- OpenAlex
- W2152142944
- Cited by
- 9
- References
- 17
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 1 · 2022: 2 · 2020: 1
- FWCI
- 0.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. National Library of Medicine
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Medicine › Epidemiology › Preterm Birth and Chorioamnionitis
- Keywords
- cervical length measurement, transvaginal ultrasound, computer-aided image analysis, speckle reduction, region-based segmentation, template matching, preterm birth prediction, intraobserver variability
- MeSH terms
- Algorithms, Artificial Intelligence, Cervix Uteri, Female, Humans, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Pregnancy, Vagina, Ultrasonography, Prenatal, Risk Assessment, Premature Birth
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