Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree
Abstract
We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.
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AI usage in standard plane acquisition has the benefit of real-time applicability.
“the possibility of real-time applicability [15,25,32,33]”
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- Authors
- Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D.
- Affiliations
- Siemens (United States) (US)
- Siemens Healthcare (United States) (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2008-09
- Volume
- 27
- Issue
- 9
- Pages
- 1342-1355
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2120023383
- Cited by
- 205
- References
- 42
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 13 · 2023: 10 · 2022: 17 · 2021: 8 · 2020: 12 · 2019: 16 · 2018: 10 · 2017: 12 · 2016: 14 · 2015: 17 · 2014: 16 · 2013: 10 · 2012: 11
- FWCI
- 12.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- 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 › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- prenatal ultrasound, automatic segmentation, fetal biometry, biparietal diameter, head circumference, abdominal circumference, femur length, humerus length, crown-rump length, speckle noise, signal dropout
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Simulation, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Models, Anatomic, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Ultrasonography, Prenatal
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