Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects
Abstract
Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.
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Posterior placental implantation presents a significant segmentation challenge because the overlying fetus can attenuate and scatter the US signal, causing shadows and image artefact.
“Placentas that implant on the posterior uterine wall present a significant segmentation challenge as the overlying fetus can attenuate and scatter the US signal causing shadows and image artefact.”
From Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment (2021) · cites this paper as [17]
The placenta has a heterogeneous appearance and can implant on any of the uterine walls.
“The placenta has a heterogeneous appearance and can implant on any of the uterine walls.”
From Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment (2021) · cites this paper as [17]
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Nurmaini, S., Rachmatullah, M. N., Sapitri, A. I., Darmawahyuni, A., Tutuko, B., Firdaus, F., Partan, R. U., & Bernolian, N. (2021) · Sensors · doi:10.3390/s21238007 · Cited by 81 · see citing works
Sendra-Balcells, C., Campello, V. M., Torrents-Barrena, J., Ahmed, Y. A., Elattar, M., Ohene-Botwe, B., Nyangulu, P., Stones, W., Ammar, M., Benamer, L. N., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-29490-3 · Cited by 57 · see citing works
Self-citationRLDS: An explainable residual learning diagnosis system for fetal congenital heart disease
Qiao, S., Pang, S., Luo, G., Pan, S., Yu, Z., Chen, T., & Lv, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.10.001 · Cited by 42 · see citing works
Torrents-Barrena, J., Monill, N., Piella, G., Gratacós, E., Eixarch, E., Ceresa, M., & González Ballester, M. A. (2021) · Academic Radiology · doi:10.1016/j.acra.2019.11.006 · Cited by 42 · see citing works
Self-citationLooney, P., Yin, Y., Collins, S. L., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Stevenson, G. N. (2021) · IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control · doi:10.1109/tuffc.2021.3052143 · Cited by 40 · see citing works
Torrents-Barrena, J., López-Velazco, R., Piella, G., Masoller, N., Valenzuela-Alcaraz, B., Gratacós, E., Eixarch, E., Ceresa, M., & Ángel González Ballester, M. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.104993 · Cited by 25 · see citing works
Self-citationTowards Whole Placenta Segmentation at Late Gestation Using Multi-view Ultrasound Images
Zimmer, V. A., Gomez, A., Skelton, E., Toussaint, N., Zhang, T., Khanal, B., Wright, R., Noh, Y., Ho, A., Matthew, J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32254-0_70 · Cited by 20 · see citing works
- Authors
- Torrents-Barrena, J., Piella, G., Masoller, N., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Hospital Sant Joan de Déu Barcelona (ES)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 51
- Pages
- 61-88
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2898050043
- Cited by
- 97
- References
- 178
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 18 · 2023: 18 · 2022: 23 · 2021: 12 · 2020: 10 · 2019: 6
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- Funders
- CELLEX
- Ministry of Economy and Competitiveness (MDM-2015-0502)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- fetal MRI, 3D ultrasound, fetal imaging, image segmentation, image classification, fetal brain, fetal lung, fetal liver, fetal heart, placenta, prenatal diagnosis, fetal structural anomalies, twin-twin transfusion syndrome, gestational diabetes mellitus, congenital heart disease, diaphragmatic hernia, ventriculomegaly, fetal organ development
- MeSH terms
- Algorithms, Female, Fetal Diseases, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Pregnancy, Prenatal Diagnosis, Ultrasonography, Prenatal
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