WAVELET ANALYSIS AND NEURAL NETWORK CLASSIFIERS TO DETECT MID-SAGITTAL SECTIONS FOR NUCHAL TRANSLUCENCY MEASUREMENT
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We propose a methodology to support the physician in the automatic identification of mid-sagittal sections of the fetus in ultrasound videos acquired during the first trimester of pregnancy. A good mid-sagittal section is a key requirement to make the correct measurement of nuchal translucency which is one of the main marker for screening of chromosomal defects such as trisomy 13, 18 and 21. NT measurement is beyond the scope of this article. The proposed methodology is mainly based on wavelet analysis and neural network classifiers to detect the jawbone and on radial symmetry analysis to detect the choroid plexus. Those steps allow to identify the frames which represent correct mid-sagittal sections to be processed. The performance of the proposed methodology was analyzed on 3000 random frames uniformly extracted from 10 real clinical ultrasound videos. With respect to a ground-truth provided by an expert physician, we obtained a true positive, a true negative and a balanced accuracy equal to 87.26%, 94.98% and 91.12% respectively.
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Three studies focused on detection of the midsagittal plane of the fetus, with one study focusing on 2D ultrasound and two studies on 3D ultrasound.
“Three studies focused on the detection of the midsagittal plane of the fetus, with one study focusing on 2D ultrasound 19 and two studies on 3D ultrasound.20,21”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [19]
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“In the faraway years the FMF proposed a new methodology based on a combination of the measurement of the nuchal translucency and the maternal age to reduce the risk of trisomy 21 (Snijders et al., 1998).”
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- Authors
- Sciortino, G., Orlandi, E., Valenti, C., & Tegolo, D.
- Affiliations
- University of Palermo (IT)
- Venue
- Image Analysis & Stereology
- Published
- 2016-04-14
- Volume
- 35
- Issue
- 2
- Pages
- 105
- ISSN
- 1580-3139 (ISSN-L)
- 1854-5165
- OpenAlex
- W2335957243
- Cited by
- 16
- References
- 30
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 1 · 2019: 4 · 2017: 4
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Image Analysis & Stereology (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Health Sciences › Dentistry › Oral Surgery › Dental Radiography and Imaging
- Keywords
- nuchal translucency, prenatal ultrasound, first trimester, wavelet analysis, neural network classifier, trisomy screening, chromosomal abnormalities
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