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HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy
Open accessAbstract
Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles’ diameter is usually in the range of 2–10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of 90% for ovaries and 81% for antral follicles, an improvement of 2% and 10%, respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of 91.01% and 76.49%, respectively.
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Two included studies, of which one had a prospective, randomized-controlled design, showed increased accuracy of follicle evaluation by using AI-assisted 2D and 3D US.
“Two included studies, of which one had a prospective, randomized-controlled design, showed increased accuracy of follicle evaluation and reduced examination time by using AI-assisted 2D and 3D US [222,223].”
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 [222]
The Results (section 3.3; page unavailable) report improved follicle segmentation with HaTU-Net compared with U-Net, supporting increased accuracy for AI-assisted ultrasound analysis. The Dataset section (2.1; page unavailable) describes this study as retrospective and based on static ultrasound images; it does not establish that this study was randomized or evaluated 3D ultrasound. The prospective randomized study mentioned in the citing sentence may be the other cited study. (LLM, conf=0.87)
Experimental — accuracy not yet measuredTwo included studies showed reduced examination time by using AI-assisted 2D and 3D US for follicle evaluation.
“Two included studies, of which one had a prospective, randomized-controlled design, showed increased accuracy of follicle evaluation and reduced examination time by using AI-assisted 2D and 3D US [222,223].”
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 [222]
The Introduction (page unavailable) says manual follicle measurement is time-consuming and proposes automation to improve clinical flow. But the Methods describe a retrospective analysis of ultrasound images, and the Results report segmentation performance, not a measured reduction in examination time. This source therefore supports AI-assisted follicle evaluation, but not the claimed time saving or a prospective randomized comparison of 2D and 3D ultrasound. (LLM, conf=0.86)
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Metadata
- Authors
- Singh, V. K., Yousef Kalafi, E., Cheah, E., Wang, S., Wang, J., Ozturk, A., Li, Q., Eldar, Y. C., Samir, A. E., & Kumar, V.
- Affiliations
- Harvard University (US)
- Center for Ultrasound Research & Translation at the Massachusetts General Hospital, Department of Radiology, Harvard Medical School, Boston, MA 02114, USA
- Third Hospital of Hebei Medical University (CN)
- Weizmann Institute of Science (IL)
- Corresponding author
- Kumar, Viksit
- ORCID
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2022-12-18
- Volume
- 12
- Issue
- 12
- Pages
- 3213
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC9777827
- OpenAlex
- W4312141010
- Cited by
- 11
- References
- 35
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 4 · 2023: 1
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 › Artificial Intelligence › AI in cancer detection
- Keywords
- antral follicle count, ovarian reserve, transvaginal ultrasound, discrete cosine transform, assisted reproduction
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