[Retracted] An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images
Open accessAbstract
Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system’s anatomical abnormalities. Standardized cerebellar assessments from large‐scale clinical datasets are required to investigate correlations between the growing cerebellum and postnatal neurodevelopmental results. These studies could uncover structural abnormalities that could be employed as indicators to forecast neurodevelopmental and growth consequences. To achieve this, higher‐throughput, precise, and impartial measures must be used to replace the existing human, semiautomatic, and advanced algorithms, which seem to be time‐consuming and inaccurate. In this article, we presented an innovative deep learning (DL) technique for automatic fetal cerebellum segmentation from 2‐dimensional (2D) US brain images. We present ReU‐Net, a semantic segmentation network tailored to the anatomy of the fetal cerebellum. Moreover, we use U‐Net as a foundation models with the incorporation of residual blocks and Wiener filter over the last 2 layers to segregate the cerebellum (c) from the noisy US data. 590 images for training and 150 images for testing were taken; also, we employed a 5‐fold cross‐assessment method. Our ReU‐Net scored 91%, 92%, 25.42, 98%, 92%, and 94% for Dice Score Coefficient (DSC), F1‐score, Hausdorff Distance (HD), accuracy, recall, and precision, correspondingly. The suggested method outperforms the other U‐Net predicated techniques by a quantitatively significant margin (p 0.001). Our presented approach can be used to allow high bandwidth imaging techniques in medical study fetal US images as well as biometric evaluation on a broader scale in fetal US images.
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Burgos-Artizzu et al. and Sreelakshmy et al. portrayed AI methods for the estimation of GA through an analysis of transthalamic axial planes or cerebellum measurements.
“Lastly, the studies of Burgos-Artizzu et al. and Sreelakshmy et al. portrayed AI methods for the estimation of GA through an analysis of transthalamic axial planes or cerebellum measurements [95,96].”
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 [96]
The paper describes a deep-learning method for segmenting the fetal cerebellum in ultrasound images, and its Abstract says cerebellar measurements are used to determine gestational age. But the Results report segmentation performance, not gestational-age estimates; the Discussion describes extending the method to cerebellar measurements as future work. Thus it supports the connection to cerebellar analysis, but not the claim that this study presented an AI method for estimating GA. (LLM, conf=0.86)
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- Authors
- Sreelakshmy, R., Titus, A., Sasirekha, N., Logashanmugam, E., Begam, R. B., Ramkumar, G., & Raju, R.
- Affiliations
- Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN)
- Jeppiaar Engineering College (IN)
- Sona College of Technology (IN)
- Sathyabama Institute of Science and Technology (IN)
- Rajalakshmi Engineering College (IN)
- Saveetha University (IN)
- St. Joseph University In Tanzania (TZ)
- Corresponding author
- Raju, Raja
- ORCID
- Venue
- BioMed Research International
- Publisher
- Wiley
- Published
- 2022-01
- Volume
- 2022
- Issue
- 1
- Pages
- 8342767-8342767
- ISSN
- 1110-7243 (ISSN-L)
- 2314-6133
- 2314-6141
- PMC
- PMC9225853
- OpenAlex
- W4283031360
- Cited by
- 51
- References
- 32
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 18 · 2023: 18 · 2022: 7
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2023-12-29)
- Open access
- hybrid
- doi.org
- Locations
- BioMed Research International (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Keywords
- prenatal ultrasound, semantic segmentation, U-Net, Wiener filter, gestational age estimation, fetal brain development, fetal brain imaging
- MeSH terms
- Deep Learning, Algorithms, Cerebellum, Female, Humans, Image Processing, Computer-Assisted, Pregnancy, Ultrasonography, Ultrasonography, Prenatal
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 ›
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