A Nature-Inspired Search Space Reduction Technique for Spine Identification on Ultrasound Samples of Spina Bifida Cases
Open accessAbstract
Spina bifida is a birth defect caused by incomplete closing around the spinal cord. Spina bifida is diagnosed in a number of different ways. One approach involves searching for a deformity in the spinal axis via ultrasound. Although easy to apply, this approach requires a highly trained clinician to locate the abnormality due to the noise and distortion present in prenatal ultrasound images. Accordingly, visual examination of ultrasound images may be error prone and subjective. A computerized support system that would automatically detect the location of the spinal deformity may be helpful to the clinician in the diagnostic process. Such a software system first and foremost would require an algorithm for the identification of the entire (healthy or unhealthy) spine in the ultrasound image. This paper introduces a novel flocking dynamics based approach for reducing the size of the search space in the spine identification problem. Proposed approach accepts bone-like blobs on the ultrasound images as bird flocks and combine them into bone groups by calculating the migration path of each flock. Presented results reveal that the method is able to locate correct bones to be grouped together and reduce search space (i.e. number of bones) up to 68%.
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Cengizler et al. proposed an algorithm for the identification of the fetal spine and proofed the model's performance in cases of fetuses with spina bifida.
“Cengizler et al. proposed an algorithm for the identification of the fetal spine and proofed the model’s performance in cases of fetuses with spina bifida [141].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [141]
The Methods section (page unavailable) says the proposed method was tested on ultrasound images of fourteen fetal spines diagnosed with spina bifida, and the Results evaluate its performance. But the Discussion and Conclusions describe it as a search-space reduction method for spine identification, not a complete spine-identification algorithm; the paper says that an appropriate fitness function would still be needed for that task. (LLM, conf=0.86)
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- Authors
- Cengizler, Ç., Ün, M. K., & Büyükkurt, S.
- Affiliations
- Cukurova University (TR)
- Corresponding author
- Cengizler, Çağlar
- Venue
- Scientific Reports
- Published
- 2020-06-09
- Volume
- 10
- Issue
- 1
- Pages
- 9280-9280
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7283309
- OpenAlex
- W3033922416
- Cited by
- 11
- References
- 25
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 2
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Spinal Dysraphism and Malformations
- Keywords
- spina bifida, prenatal ultrasound, search space reduction, flocking dynamics, automated diagnosis, bio-inspired algorithms
- MeSH terms
- Machine Learning, Algorithms, Diagnosis, Computer-Assisted, Female, Humans, Image Interpretation, Computer-Assisted, Pregnancy, Prenatal Diagnosis, Spinal Cord, Spina Bifida Cystica, Ultrasonography, Prenatal
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