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

Authors :
Torrents-Barrena J
Piella G
Masoller-Casas N
Gratacós E
Eixarch E
Ceresa M
Ballester MAG
Source :
MEDICAL IMAGE ANALYSIS, r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu, instname, r-FSJD: Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu, Fundació Sant Joan de Déu
Publication Year :
2019
Publisher :
Elsevier BV, 2019.

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.

Details

ISSN :
13618415
Database :
OpenAIRE
Journal :
MEDICAL IMAGE ANALYSIS, r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu, instname, r-FSJD: Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu, Fundació Sant Joan de Déu
Accession number :
edsair.dedup.wf.001..14c640195c094934201e6a1c1bee2df2