Cited by 97Medical Image Segmentation TechniquesFetal and Pediatric Neurological DisordersArtificial Intelligence in Healthcare and Education

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects

Torrents-Barrena, Jordina, Piella, Gemma, Masoller, Narcís, Gratacós, Eduard, Eixarch, Elisenda, Ceresa, Mario, Ballester, Miguel Ángel González·2019·Medical Image Analysis

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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Crossref · 10.1016/j.media.2018.10.003

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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)
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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