Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks
Abstract
Automatic analysis of fetal echocardiography screening images could aid in the identification of congenital heart diseases. The first step towards automatic fetal echocardiography analysis is locating the fetal heart in an image and identifying the viewing (imaging) plane. This is highly challenging since the fetal heart is small with relatively indistinct anatomical structural appearance. This is further compounded by the presence of artefacts in ultrasound images. Herein we provide a state-of-art solution for detecting the fetal heart and classifying each individual frame as belonging to one of the standard viewing planes using fully convolutional neural networks (FCNs). Our FCN model achieves a classification error rate of 23.48% on real-world clinical ultrasound data. We also present comparative performance for analysis of different FCN architectures.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Dong et al. demonstrated a mean average precision of 94.52% for quality control of the four-chamber view.
“Three studies proposed automated systems for detection and evaluation of the fetal four-chamber view whereas Dong et al. proposed a deep learning framework for quality control of the four-chamber view, demonstrating a mean average precision of 94.52%.80-83”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [83]
The citation lists haven’t been fetched yet
References 13 · Cited by 58
- Authors
- Sundaresan, V., Bridge, C. P., Ioannou, C., & Noble, J. A.
- Affiliations
- University of Oxford (GB)
- John Radcliffe Hospital (GB)
- Venue
- 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
- Publisher
- IEEE
- Published
- 2017-04
- Pages
- 671-674
- OpenAlex
- W2640545234
- Cited by
- 58
- References
- 13
- Citations by year
- 2025: 3 · 2024: 10 · 2023: 7 · 2022: 11 · 2021: 9 · 2020: 7 · 2019: 6 · 2018: 5
- FWCI
- 1.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- fetal echocardiography, congenital heart disease screening, ultrasonography, fully convolutional network, ultrasound artefacts, automated image analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
